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=
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Ò@FqÁïãŠrÉéœh“ÅŒwÍR¢B„!C˜ )ý-!͏?P¡é™a ÑmR~×Pü/sJ†zp%*Ìcð–d	ž2mF£ v>ºóùÀv«´MQe!d©JS…ùáØЙ:o?«žrœ³¸¥=ë}«ogŸ’fx~›FÎd¡BÄpä_k/=§Õzœ<ƒÀ¥9–¨ß˥'?d1þrÐÏ SúÅàU÷«ӕúòûõò•éM{ÑuçS»íÆuç{ݪr¯d§5{É]wÌåå ƏwìŸnÝõ«×>³·]X®—7«‹UP
N;צO-³‡n¯^ûÂw«<ӷîër¡\©1llÏÞwï"×Niª^…ÀÏí~%nmàKqµ^_éÔËM‡vÉû¼ºþ4²;¶¶áø†¹½†áU|æVœº:}`WmߎÜò_†՗ÕNe^çÒl•¿W·_Fǃzù+·‡	›r»z~Ü?ÿ<?ùëñÉcv†õÒå£;våÅB½xéè»éÞÉâ7nüꏅï}l±¬^º:}dC·±RÁé҇Gã
¨‡P
        _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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A,0“ëûÛ¼¦ß¢e†í‡Y±ñ–2„ì1²5*# ´ÅÃВãrQSËü_	© $üŽ,“n2F%G^ÁÌTüº8¨ò<0fH¨%àW®´„s6ÚsL쐳ˆñŸO€§î…¨}«n¾ýÙ@=Yéo<
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j¶œ>ÍÂÓªˆûxH¾ý…Rœ…¢øøy•Aý/{j|KŠ^‰-&Ž•$mpxػëBmñû͈ŠÅl>«dJuýú}ª
é÷Ò<¶\ÈÈ8µÅr
+¹4Úyä÷3>U*#†|øÈ\³m&Æíþçgʈö·{™-oÖӳBjŒR¹"¶ò
9ӇÔ_Î7ۮíôÂÞI%ÿSŽZCžçªA@vpî£)^½Y³G°¢ZÍ:0-*¸•W3·J¦j•†¢ß^ÿeè֌ì`³^c°œcM§á(%Á~Ö \wßML¤-r2-ɰ¼Eуè";ä`Ús)	=¡¹(–BsÖreëÊR€Wäî#_mt19937nameloader__loader__origin__file__parent__package__submodule_search_locations__path__Interpreter change detected - this module can only be loaded into one interpreter per process.Module '_mt19937' has already been imported. Re-initialisation is not supported.builtinscython_runtime__builtins__numpy.random._mt19937init numpy.random._mt19937numpy/random/_mt19937.pyx../numpy/__init__.cython-30.pxd<stringsource>compile time Python version %d.%d of module '%.100s' %s runtime version %d.%ddoes not matchzlibdecompressFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'._cython_3_2_5Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling_cython_3_2_5._common_types_metatype__module___cython_3_2_5.cython_function_or_method<cyfunction %U at %p>unbound method %.200S() needs an argumentkeywords must be stringstakes no argumentstakes exactly one argumentBad call flags for CyFunctiontakes no keyword arguments%.200s() %s (%zd given)%.200s() %s__reduce____vectorcalloffset____weaklistoffset__func_doc__doc__func_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutine__name__ must be set to a string object__qualname__ must be set to a string object__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__annotations__ must be set to a dict objectnumpy.random.bit_generatorBitGenerator%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectnumpy.random._mt19937.MT19937.jump_inplacenumpy.random._mt19937.MT19937MT19937(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.

    _legacy_seedingjumped__reduce_cython____setstate_cython__numpy.random._mt19937.MT19937._legacy_seeding%s() got an unexpected keyword argument '%U'%s() got multiple values for keyword argument '%U'%.200s() keywords must be stringsat leastat mostexactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)s while calling a Python objectNULL result without error in PyObject_Callvalue too large to convert to uint32_tcan't convert negative value to uint32_tan integer is required__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.__int__ returned non-int (type %.200s)name '%U' is not definedinstance exception may not have a separate valuecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionMissing type objectCannot convert %.200s to %.200snumpy.random._mt19937.MT19937.jumpednumpy.random._mt19937.MT19937.__reduce_cython__numpy.random._mt19937.MT19937.__setstate_cython__state
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        numpy.random._mt19937.MT19937.state.__get____del__numpy.random._mt19937.MT19937.state.__set__join() result is too long for a Python stringvalue too large to convert to int__init__numpy.random._mt19937.MT19937.__init__base class '%.200s' is not a heap typeextension 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 typemultiple bases have vtable conflict: '%.200s' and '%.200s'invalid vtable found for imported typeUnable to initialize pickling for %.200stypenumpydtypeflatiterbroadcastndarraygenericnumberintegersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSeedSequenceSeedlessSequencenumpy.import_arraynumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is not PyCapsule object_ARRAY_API is NULL pointermodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule was compiled against NumPy C-API version 0x%x (NumPy 1.23) 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.FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimeneeds an argument
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