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ÿÿFormat string allocated too short.
    Abstract base class for seed sequences that can spawn.
    seedless SeedSequences cannot generate statenumpy.core.umath failed to importThe size of the entropy pool should be at least SeedSequence expects int or sequence of ints for entropy not {}Not implemented in base BitGeneratorNon-native byte order not supportedIncompatible checksums (%s vs 0xf88304a = (entropy, n_children_spawned, pool, pool_size, spawn_key))Incompatible checksums (%s vs 0xd41d8cd = ())Format string allocated too short, see comment in numpy.pxdBitGenerator is a base class and cannot be instantizedunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to import 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)
    
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
http://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.

    Abstract base class for seed sequences.

    ``BitGenerator`` implementations should treat any object that adheres to
    this interface as a seed sequence.

    See Also
    --------
    SeedSequence, SeedlessSeedSequence
    __pyx_unpickle_SeedlessSeedSequence_coerce_to_uint32_array (line 88)only support uint32 or uint64expected non-negative integerISpawnableSeedSequence.spawnISeedSequence.generate_statenumpy.random.bit_generator__pyx_unpickle_SeedSequenceunrecognized seed string_coerce_to_uint32_arrayISpawnableSeedSequenceseed must be integer    n_children_spawned=SeedlessSeedSequence_int_to_uint32_arrayNotImplementedErrorn_children_spawnedcline_in_traceback__bit_generator_ctorbit_generator.pyxSeedlessSequenceunsignedinteger__setstate_cython____pyx_PickleErrordummy_threadinggenerate_stateabstractmethod__reduce_cython__bit_generatorISeedSequencestringsource__pyx_checksumSystemRandomSeedSequenceRuntimeErrorBitGenerator    spawn_key=    pool_size=getrandbitsconcatenatePickleErrorImportErrorstartswith__pyx_vtable____pyx_resultn_childrenValueErrorDECIMAL_REthreadingspawn_key__reduce_ex____pyx_statepool_size__metaclass__itertools    entropy=TypeError__setstate__registerrandbits__qualname____pyx_typepickle__getstate__errstatesubseqssecrets__prepare__n_wordsintegerinexactentropycompileupdateuint64uint32__reduce__random_pickleoutput__module__method__import__ignoreformatastypezerosstatespawnrangenumpymatchdtypecyclearrayview__test__sizeselfseedover__name____main____dict__copybaseLocksys__new____doc__cntarr__all__abc
ABC[0-9]+<u8<u4renp),(0xxvnBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'Does not understand character buffer dtype format string ('%c')%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)Bad call flags in __Pyx_CyFunction_Call. METH_OLDARGS is no longer supported!%.200s() takes no keyword arguments%.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 PyObjectUnable to initialize pickling for %s%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict object__defaults__ must be set to a tuple objectfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__qualname__ must be set to a string object__name__ must be set to a string objectmetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesnumpy.random.bit_generator.__defaults__numpy.random.bit_generator.SeedlessSeedSequence.spawnnumpy.random.bit_generator.SeedSequence.state.__get__numpy.random.bit_generator.BitGenerator.cffi.__get__numpy.random.bit_generator.BitGenerator.ctypes.__get__numpy.random.bit_generator.BitGenerator.__reduce__numpy.random.bit_generator.BitGenerator.__setstate__numpy.random.bit_generator.BitGenerator.__getstate__numpy.random.bit_generator.SeedSequence.n_children_spawned.__get__numpy.random.bit_generator.SeedSequence.pool_size.__get__Unexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Buffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)Out of bounds on buffer access (axis %d)numpy.random.bit_generator.SeedSequence.mix_entropy%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'numpy.random.bit_generator.BitGenerator.random_raw%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random.bit_generator.BitGenerator._benchmarknumpy.random.bit_generator.ISeedSequence.generate_statenumpy.random.bit_generator.ISpawnableSeedSequence.spawn__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)can't convert negative value to uint32_tvalue too large to convert to uint32_tcalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionnumpy.random.bit_generator.BitGenerator.state.__get__numpy.random.bit_generator.BitGenerator.state.__set__numpy.random.bit_generator.SeedlessSeedSequence.generate_statenumpy.random.bit_generator.SeedSequence.__repr__numpy.random.bit_generator.SeedlessSeedSequence.__reduce_cython__numpy.random.bit_generator.SeedSequence.__reduce_cython__numpy.random.bit_generator.BitGenerator.__init__object of type 'NoneType' has no len()hasattr(): attribute name must be stringnumpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence__set_statenumpy.random.bit_generator.SeedlessSeedSequence.__setstate_cython____pyx_unpickle_SeedlessSeedSequencenumpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequencenumpy.random.bit_generator.SeedSequence.get_assembled_entropynumpy.random.bit_generator._int_to_uint32_arrayCannot convert %.200s to %.200snumpy.random.bit_generator.SeedSequence.__init__numpy.random.bit_generator.SeedSequence.spawnnumpy.random.bit_generator._coerce_to_uint32_array'NoneType' object is not subscriptablenumpy.random.bit_generator.__pyx_unpickle_SeedSequence__set_statenumpy.random.bit_generator.__pyx_unpickle_SeedSequencenumpy.random.bit_generator.SeedSequence.__setstate_cython__numpy.random.bit_generator.SeedSequence.generate_state%.200s object is not an iteratorcompiletime version %s of module '%.100s' does not match runtime version %sShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingPyObject *(bitgen_t *, PyObject *, Py_ssize_t, PyObject *)PyObject *(bitgen_t *, PyObject *, PyObject *, PyObject *)_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 runtimeinit numpy.random.bit_generatornumpy.random.bit_generator.SeedlessSeedSequence
    A seed sequence for BitGenerators with no need for seed state.

    See Also
    --------
    SeedSequence, ISeedSequence
    numpy.random.bit_generator.SeedlessSequencenumpy.random.bit_generator.SeedSequence
    SeedSequence(entropy=None, *, spawn_key=(), pool_size=4)

    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`.
    spawn_key : {(), sequence[int]}, optional
        A third source of entropy, used internally when calling
        `SeedSequence.spawn`
    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.bit_generator.BitGenerator
    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.

    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
    
        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
        
        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
        
        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
        
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 ="6ZFZVZfZvZ†Z–Z¦Z¶ZÆZÖZæZöZ[[&[6[F[V[f[v[†[–[¦[¶[Æ[Ö[æ[ö[\\&\6\F\V\f\v\†\–\¦\¶\Æ\Ö\æ\ö\]]&]6]F]V]f]v]†]–]¦]¶]Æ]Ö]æ]ö]^^&^6^F^V^f^v^†^–^¦^¶^Æ^Ö^æ^ö^__&_6_F_V_f_v_†_–_¦_¶_Æ_Ö_æ_ö_``&`6`F`V`f`v`†`–`¦`¶`Æ`Ö`æ`ö`aa&a6aFaVafaUsed in tests
        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 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.
        
        spawn(n_children)

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

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

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

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

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