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dhlptx|€„ˆŒ”˜ œ! "¤#¨$¬%°&´'¸(¼*À+Ä,È/Ì0Ð1Ô2Ø3Ü4à5ä6è7ì8ð9ô:ø>ü?@ABCDEFG H$L(M,N0O4P8T<U@VDWHXLYPZT[X\\]`^d_h`lapbtcxd|e€f„gˆhŒij”k˜lœm n¤o¨p¬q°r´s¸u¼wÀxÄyÈzÌ{Ð|Ô}Ø~܁àƒä„è…ì†ð‡ôˆøŠü‹			‘	“	”	–	—	™ 	š$	›(	œ,	0	ž4	Ÿ8	 <	¡@	¢D	¥H	§L	¨P	©T	ªX	«\	¬`	­d	®h	¯l	°p	±t	²x	³|	µ€	¶„	·ˆ	¹Œ	ºexactlycffiMissing type objectnumpy.random.bit_generator.SeedlessSeedSequencenumpy.random.bit_generator.SeedlessSeedSequence.__reduce_cython__name '%U' is not definednumpy.random.bit_generator.BitGenerator.cffi.__get__Expected a dimension of size %zu, got %zu__setstate_cython__tupledtypenumpy.random.bit_generator.__pyx_unpickle_SeedSequence__name__ must be set to a string object_benchmark%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.
        Get or set the PRNG state

        The base BitGenerator.state must be overridden by a subclass

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        'long''unsigned long'00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899numpy.random.bit_generator.__pyx_defaultsndarraynumpy.random.bit_generator._int_to_uint32_arraydoes not matchShared Cython type %.200s has the wrong size, try recompiling%.200s() %s__setstate__numpy.random.bit_generator.BitGenerator.random_rawnumpy.random.bit_generator.BitGenerator.__init__'double''long double'strings are too large to concatpool_sizepoolnumpy.random.bit_generator.SeedlessSeedSequence.__setstate_cython__flexiblenumpy.core._multiarray_umathFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'._cython_3_2_6numpy.random.bit_generator.BitGenerator.__getstate__%.200s() keywords must be stringsBuffer dtype mismatch, expected %s%s%s but got %scomplexfloatingmetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesModule 'bit_generator' has already been imported. Re-initialisation is not supported.zlib__qualname__numpy.random.bit_generator.BitGenerator.state.__get__multiple bases have vtable conflict: '%.200s' and '%.200s'integernumpy.random.bit_generator.ISeedSequence.generate_stateloader__package__<stringsource>__vectorcalloffset__func_doc__dict____annotate____getstate__at most%.200s() takes %.8s %zd positional argument%.1s (%zd given)_ctypesnumpy.random.bit_generator.SeedSequence.generate_stateSeedlessSeedSequence()
--

    A seed sequence for BitGenerators with no need for seed state.

    See Also
    --------
    SeedSequence, ISeedSequence
    floatingvariablean integer is requirednumpy.random.bit_generator.BitGenerator._benchmark'Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'Out of bounds on buffer access (axis %d)numpy._core._multiarray_umath_ARRAY_API is NULL pointerinit numpy.random.bit_generatornumpy.random.bit_generator.BitGenerator.__setstate__state_seed_seqExpected a dimension of size %zu, got %dnumpy.random.bit_generator.SeedSequence.__repr__numpy.random.bit_generator.SeedSequence.__setstate_cython___cython_3_2_6._common_types_metatypesnumpy.random.bit_generator.BitGenerator.__reduce__Python objectnumpy.random.bit_generator.SeedSequence.pool_size.__get___ARRAY_APIFATAL: module compiled as unknown endiantakes no arguments__weaklistoffset__func_closure'complex double'a structneeds an argument__mro_entries__ must return a tuple__file__parentsubmodule_search_locationsnumpy/random/bit_generator.pxd%.200s() %s (%zd given)npy_uint32generate_state%.200s object is not an iteratorfunctionArgument '%.200s' has incorrect type (expected %.200s, got %.200s)func_dict'long long'__reduce_cython__Expected %s, got %.200snumpy.random.bit_generator.__pyx_unpickle_SeedSequence__set_stateNULL result without error in PyObject_Call__loader__func_name__globals__ctypes
        CFFI interface

        Returns
        -------
        interface : namedtuple
            Named tuple containing CFFI wrapper

            * state_address - Memory address of the state struct
            * state - pointer to the state struct
            * next_uint64 - function pointer to produce 64 bit integers
            * next_uint32 - function pointer to produce 32 bit integers
            * next_double - function pointer to produce doubles
            * bitgen - pointer to the bit generator struct
        'bool''short'numpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence__set_stateC %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)__name__BitGenerator(seed=None)
--

    Base Class for generic BitGenerators, which provide a stream
    of random bits based on different algorithms. Must be overridden.

    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
        `~numpy.random.SeedSequence` to derive the initial `BitGenerator` state.
        One may also pass in a `SeedSequence` instance.
        All integer values must be non-negative.

    Attributes
    ----------
    lock : threading.Lock
        Lock instance that is shared so that the same BitGenerator 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.

    See Also
    --------
    SeedSequence
    seed_seqlock'unsigned short''unsigned int''unsigned long long'Unexpected format string character: '%c'flatiter__defaults__changes to cyfunction.__kwdefaults__ will not currently affect the values used in function callsnumpy.random.bit_generator.BitGeneratornumpy.random.bit_generator.BitGenerator.ctypes.__get__'float'a pointernumpy.random.bit_generator.SeedSequence.get_assembled_entropycannot pass None into a C function argument that is declared 'not None'numpy.random.bit_generator.SeedSequence.state.__get__Unexpected end of format string, expected ')'numpy.random.bit_generator.SeedSequence.__reduce_cython__n_children_spawnednumberPickleErrorcython_runtimetakes no keyword arguments__init__'int'%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectcannot import name %Snumpy.random.bit_generator.__defaults____path__spawn
        Get the seed sequence used to initialize the bit generator.

        .. versionadded:: 1.25.0

        Returns
        -------
        seed_seq : ISeedSequence
            The SeedSequence object used to initialize the BitGenerator.
            This is normally a `np.random.SeedSequence` instance.

        spawn_keyinvalid vtable found for imported typecharacterufuncmodule compiled against ABI version 0x%x but this version of numpy is 0x%xnumpy.random.bit_generator.ISpawnableSeedSequence.spawnShared Cython type %.200s is not a type object__code____kwdefaults__ must be set to a dict object'complex float'Unable to initialize pickling for %.200snumpy.random.bit_generator.SeedlessSequenceorigincompile time Python version %d.%d of module '%.100s' %s runtime version %d.%dnumpy/random/bit_generator.pyx../numpy/__init__.cython-30.pxd__int__ returned non-int (type %.200s)Expected %d dimension(s), got %dcan't convert negative value to uint32_tnumpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequenceIncompatible checksums (0x%x vs (0x%x, 0x%x, 0x%x) = (%s))_is_coroutineBuffer acquisition: Expected '{' after 'T'Does not understand character buffer dtype format string ('%c')numpy_ARRAY_API is not PyCapsule object__pyx_statefunc_globalsfunc_codePlaceholder __annotate__ function to allow 'functools.wraps' to work on Cython functions.cython __annotate__ called without 'self' argumentcalling %R should have returned an instance of BaseException, not %R'complex long double'unparsable format string%.200s.%.200s is not a type objectname__orig_bases__takes exactly one argument__reduce____closure____qualname__ must be set to a string objectat least_cffia stringExpected a comma in format string, got '%c'numpy.random.bit_generator.SeedSequenceunsignedinteger%.200s does not export expected C %.8s %.200snumpy.import_array__pyx_unpickle_SeedlessSeedSequencebuiltinsfunc_defaults__defaults__ must be set to a tuple objectnumpy.random.bit_generator.BitGenerator.spawncapsuleextension 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 typeItem size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)typebit_generatordecompressunbound method %.200S() needs an argument__annotations__ must be set to a dict objectnumpy.random.bit_generator.SeedSequence.mix_entropyendnumpy.random.bit_generator.SeedSequence.spawnnumpy.random.bit_generator.SeedSequence.n_children_spawned.__get__numpy.random.bit_generator.SeedlessSeedSequence.spawnbroadcastFATAL: module compiled as little endian, but detected different endianness at runtime__pyx_unpickle_SeedSequencenumpy.random.bit_generator__doc__changes to cyfunction.__defaults__ will not currently affect the values used in function callsrandom_rawbase class '%.200s' is not a heap typeBig-endian buffer not supported on little-endian compiler'unsigned char'Buffer dtype mismatch; next field is at offset %zd but %zd expectedSeedSequence(entropy=None, *, spawn_key=(), pool_size=4, n_children_spawned=0)
--

    SeedSequence mixes sources of entropy in a reproducible way to set the
    initial state for independent and very probably non-overlapping
    BitGenerators.

    Once the SeedSequence is instantiated, you can call the `generate_state`
    method to get an appropriately sized seed. Calling `spawn(n) <spawn>` will
    create ``n`` SeedSequences that can be used to seed independent
    BitGenerators, i.e. for different threads.

    Parameters
    ----------
    entropy : {None, int, sequence[int]}, optional
        The entropy for creating a `SeedSequence`.
        All integer values must be non-negative.
    spawn_key : {(), sequence[int]}, optional
        An additional source of entropy based on the position of this
        `SeedSequence` in the tree of such objects created with the
        `SeedSequence.spawn` method. Typically, only `SeedSequence.spawn` will
        set this, and users will not.
    pool_size : {int}, optional
        Size of the pooled entropy to store. Default is 4 to give a 128-bit
        entropy pool. 8 (for 256 bits) is another reasonable choice if working
        with larger PRNGs, but there is very little to be gained by selecting
        another value.
    n_children_spawned : {int}, optional
        The number of children already spawned. Only pass this if
        reconstructing a `SeedSequence` from a serialized form.

    Notes
    -----

    Best practice for achieving reproducible bit streams is to use
    the default ``None`` for the initial entropy, and then use
    `SeedSequence.entropy` to log/pickle the `entropy` for reproducibility:

    >>> sq1 = np.random.SeedSequence()
    >>> sq1.entropy
    243799254704924441050048792905230269161  # random
    >>> sq2 = np.random.SeedSequence(sq1.entropy)
    >>> np.all(sq1.generate_state(10) == sq2.generate_state(10))
    True
    numpy.random._commonmodule 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._coerce_to_uint32_arrayInterpreter change detected - this module can only be loaded into one interpreter per process.__builtins____module__<cyfunction %U at %p>__kwdefaults__%s() got an unexpected keyword argument '%U'raise: exception class must be a subclass of BaseException__del__'signed char' while calling a Python objectpickle_cython_3_2_6.cython_function_or_methodBad call flags for CyFunction
        ctypes interface

        Returns
        -------
        interface : namedtuple
            Named tuple containing ctypes wrapper

            * state_address - Memory address of the state struct
            * state - pointer to the state struct
            * next_uint64 - function pointer to produce 64 bit integers
            * next_uint32 - function pointer to produce 32 bit integers
            * next_double - function pointer to produce doubles
            * bitgen - pointer to the bit generator struct
        Expected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Cannot convert %.200s to %.200s__debug____annotate__ cannot be deletednumpy.random.bit_generator.BitGenerator.state.__set__buffer dtype'char'entropynumpy.random.bit_generator.SeedSequence.__init__numpy.random.bit_generator.SeedlessSeedSequence.generate_statesignedintegerinexact_int_to_uint32_arraykeywords must be strings__annotations__Buffer has wrong number of dimensions (expected %d, got %d)Cannot handle repeated arrays in format stringjoin() result is too long for a Python stringgeneric__pyx_capi__numpy.random.bit_generator._coerce_to_uint32_array
BitGenerator base class and SeedSequence used to seed the BitGenerators.

SeedSequence is derived from Melissa E. O'Neill's C++11 `std::seed_seq`
implementation, as it has a lot of nice properties that we want.

https://gist.github.com/imneme/540829265469e673d045
https://www.pcg-random.org/posts/developing-a-seed_seq-alternative.html

The MIT License (MIT)

Copyright (c) 2015 Melissa E. O'Neill
Copyright (c) 2019 NumPy Developers

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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        spawn(n_children)

        Create new independent child bit generators.

        See :ref:`seedsequence-spawn` for additional notes on spawning
        children.  Some bit generators also implement ``jumped``
        as a different approach for creating independent streams.

        .. versionadded:: 1.25.0

        Parameters
        ----------
        n_children : int

        Returns
        -------
        child_bit_generators : list of BitGenerators

        Raises
        ------
        TypeError
            When the underlying SeedSequence does not implement spawning.

        See Also
        --------
        random.Generator.spawn, random.SeedSequence.spawn :
            Equivalent method on the generator and seed sequence.

        
        random_raw(self, size=None)

        Return randoms as generated by the underlying BitGenerator

        Parameters
        ----------
        size : int or tuple of ints, optional
            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
            ``m * n * k`` samples are drawn.  Default is None, in which case a
            single value is returned.
        output : bool, optional
            Output values.  Used for performance testing since the generated
            values are not returned.

        Returns
        -------
        out : uint or ndarray
            Drawn samples.

        Notes
        -----
        This method directly exposes the raw underlying pseudo-random
        number generator. All values are returned as unsigned 64-bit
        values irrespective of the number of bits produced by the PRNG.

        See the class docstring for the number of bits returned.
        Used in testsBitGenerator
        generate_state(n_words, dtype=np.uint32)

        Return the requested number of words for PRNG seeding.

        A BitGenerator should call this method in its constructor with
        an appropriate `n_words` parameter to properly seed itself.

        Parameters
        ----------
        n_words : int
        dtype : np.uint32 or np.uint64, optional
            The size of each word. This should only be either `uint32` or
            `uint64`. Strings (`'uint32'`, `'uint64'`) are fine. Note that
            requesting `uint64` will draw twice as many bits as `uint32` for
            the same `n_words`. This is a convenience for `BitGenerator`\ s
            that express their states as `uint64` arrays.

        Returns
        -------
        state : uint32 or uint64 array, shape=(n_words,)
        
        spawn(n_children)

        Spawn a number of child `SeedSequence` s by extending the
        `spawn_key`.

        See :ref:`seedsequence-spawn` for additional notes on spawning
        children.

        Parameters
        ----------
        n_children : int

        Returns
        -------
        seqs : list of `SeedSequence` s

        See Also
        --------
        random.Generator.spawn, random.BitGenerator.spawn :
            Equivalent method on the generator and bit generator.

         Coerce an input to a uint32 array.

    If a `uint32` array, pass it through directly.
    If a non-negative integer, then break it up into `uint32` words, lowest
    bits first.
    If a string starting with "0x", then interpret as a hex integer, as above.
    If a string of decimal digits, interpret as a decimal integer, as above.
    If a sequence of ints or strings, interpret each element as above and
    concatenate.

    Note that the handling of `int64` or `uint64` arrays are not just
    straightforward views as `uint32` arrays. If an element is small enough to
    fit into a `uint32`, then it will only take up one `uint32` element in the
    output. This is to make sure that the interpretation of a sequence of
    integers is the same regardless of numpy's default integer type, which
    differs on different platforms.

    Parameters
    ----------
    x : int, str, sequence of int or str

    Returns
    -------
    seed_array : uint32 array

    Examples
    --------
    >>> import numpy as np
    >>> from numpy.random.bit_generator import _coerce_to_uint32_array
    >>> _coerce_to_uint32_array(12345)
    array([12345], dtype=uint32)
    >>> _coerce_to_uint32_array('12345')
    array([12345], dtype=uint32)
    >>> _coerce_to_uint32_array('0x12345')
    array([74565], dtype=uint32)
    >>> _coerce_to_uint32_array([12345, '67890'])
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array(np.array([12345, 67890], dtype=np.uint32))
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array(np.array([12345, 67890], dtype=np.int64))
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array([12345, 0x10deadbeef, 67890, 0xdeadbeef])
    array([     12345, 3735928559,         16,      67890, 3735928559],
          dtype=uint32)
    >>> _coerce_to_uint32_array(1234567890123456789012345678901234567890)
    array([3460238034, 2898026390, 3235640248, 2697535605,          3],
          dtype=uint32)
    
        generate_state(n_words, dtype=np.uint32)

        Return the requested number of words for PRNG seeding.

        A BitGenerator should call this method in its constructor with
        an appropriate `n_words` parameter to properly seed itself.

        Parameters
        ----------
        n_words : int
        dtype : np.uint32 or np.uint64, optional
            The size of each word. This should only be either `uint32` or
            `uint64`. Strings (`'uint32'`, `'uint64'`) are fine. Note that
            requesting `uint64` will draw twice as many bits as `uint32` for
            the same `n_words`. This is a convenience for `BitGenerator`\ s that
            express their states as `uint64` arrays.

        Returns
        -------
        state : uint32 or uint64 array, shape=(n_words,)
        
        spawn(n_children)

        Spawn a number of child `SeedSequence` s by extending the
        `spawn_key`.

        See :ref:`seedsequence-spawn` for additional notes on spawning
        children.

        Parameters
        ----------
        n_children : int

        Returns
        -------
        seqs : list of `SeedSequence` s
        entropy, n_children_spawned, pool, pool_size, spawn_keyŸåà¤Kêx
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