/* Generated by Cython 0.29.21 */

#define PY_SSIZE_T_CLEAN
#include "Python.h"
#ifndef Py_PYTHON_H
    #error Python headers needed to compile C extensions, please install development version of Python.
#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
    #error Cython requires Python 2.6+ or Python 3.3+.
#else
#define CYTHON_ABI "0_29_21"
#define CYTHON_HEX_VERSION 0x001D15F0
#define CYTHON_FUTURE_DIVISION 1
#include <stddef.h>
#ifndef offsetof
  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
#endif
#if !defined(WIN32) && !defined(MS_WINDOWS)
  #ifndef __stdcall
    #define __stdcall
  #endif
  #ifndef __cdecl
    #define __cdecl
  #endif
  #ifndef __fastcall
    #define __fastcall
  #endif
#endif
#ifndef DL_IMPORT
  #define DL_IMPORT(t) t
#endif
#ifndef DL_EXPORT
  #define DL_EXPORT(t) t
#endif
#define __PYX_COMMA ,
#ifndef HAVE_LONG_LONG
  #if PY_VERSION_HEX >= 0x02070000
    #define HAVE_LONG_LONG
  #endif
#endif
#ifndef PY_LONG_LONG
  #define PY_LONG_LONG LONG_LONG
#endif
#ifndef Py_HUGE_VAL
  #define Py_HUGE_VAL HUGE_VAL
#endif
#ifdef PYPY_VERSION
  #define CYTHON_COMPILING_IN_PYPY 1
  #define CYTHON_COMPILING_IN_PYSTON 0
  #define CYTHON_COMPILING_IN_CPYTHON 0
  #undef CYTHON_USE_TYPE_SLOTS
  #define CYTHON_USE_TYPE_SLOTS 0
  #undef CYTHON_USE_PYTYPE_LOOKUP
  #define CYTHON_USE_PYTYPE_LOOKUP 0
  #if PY_VERSION_HEX < 0x03050000
    #undef CYTHON_USE_ASYNC_SLOTS
    #define CYTHON_USE_ASYNC_SLOTS 0
  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
    #define CYTHON_USE_ASYNC_SLOTS 1
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  #undef CYTHON_USE_PYLIST_INTERNALS
  #define CYTHON_USE_PYLIST_INTERNALS 0
  #undef CYTHON_USE_UNICODE_INTERNALS
  #define CYTHON_USE_UNICODE_INTERNALS 0
  #undef CYTHON_USE_UNICODE_WRITER
  #define CYTHON_USE_UNICODE_WRITER 0
  #undef CYTHON_USE_PYLONG_INTERNALS
  #define CYTHON_USE_PYLONG_INTERNALS 0
  #undef CYTHON_AVOID_BORROWED_REFS
  #define CYTHON_AVOID_BORROWED_REFS 1
  #undef CYTHON_ASSUME_SAFE_MACROS
  #define CYTHON_ASSUME_SAFE_MACROS 0
  #undef CYTHON_UNPACK_METHODS
  #define CYTHON_UNPACK_METHODS 0
  #undef CYTHON_FAST_THREAD_STATE
  #define CYTHON_FAST_THREAD_STATE 0
  #undef CYTHON_FAST_PYCALL
  #define CYTHON_FAST_PYCALL 0
  #undef CYTHON_PEP489_MULTI_PHASE_INIT
  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
  #undef CYTHON_USE_TP_FINALIZE
  #define CYTHON_USE_TP_FINALIZE 0
  #undef CYTHON_USE_DICT_VERSIONS
  #define CYTHON_USE_DICT_VERSIONS 0
  #undef CYTHON_USE_EXC_INFO_STACK
  #define CYTHON_USE_EXC_INFO_STACK 0
#elif defined(PYSTON_VERSION)
  #define CYTHON_COMPILING_IN_PYPY 0
  #define CYTHON_COMPILING_IN_PYSTON 1
  #define CYTHON_COMPILING_IN_CPYTHON 0
  #ifndef CYTHON_USE_TYPE_SLOTS
    #define CYTHON_USE_TYPE_SLOTS 1
  #endif
  #undef CYTHON_USE_PYTYPE_LOOKUP
  #define CYTHON_USE_PYTYPE_LOOKUP 0
  #undef CYTHON_USE_ASYNC_SLOTS
  #define CYTHON_USE_ASYNC_SLOTS 0
  #undef CYTHON_USE_PYLIST_INTERNALS
  #define CYTHON_USE_PYLIST_INTERNALS 0
  #ifndef CYTHON_USE_UNICODE_INTERNALS
    #define CYTHON_USE_UNICODE_INTERNALS 1
  #endif
  #undef CYTHON_USE_UNICODE_WRITER
  #define CYTHON_USE_UNICODE_WRITER 0
  #undef CYTHON_USE_PYLONG_INTERNALS
  #define CYTHON_USE_PYLONG_INTERNALS 0
  #ifndef CYTHON_AVOID_BORROWED_REFS
    #define CYTHON_AVOID_BORROWED_REFS 0
  #endif
  #ifndef CYTHON_ASSUME_SAFE_MACROS
    #define CYTHON_ASSUME_SAFE_MACROS 1
  #endif
  #ifndef CYTHON_UNPACK_METHODS
    #define CYTHON_UNPACK_METHODS 1
  #endif
  #undef CYTHON_FAST_THREAD_STATE
  #define CYTHON_FAST_THREAD_STATE 0
  #undef CYTHON_FAST_PYCALL
  #define CYTHON_FAST_PYCALL 0
  #undef CYTHON_PEP489_MULTI_PHASE_INIT
  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
  #undef CYTHON_USE_TP_FINALIZE
  #define CYTHON_USE_TP_FINALIZE 0
  #undef CYTHON_USE_DICT_VERSIONS
  #define CYTHON_USE_DICT_VERSIONS 0
  #undef CYTHON_USE_EXC_INFO_STACK
  #define CYTHON_USE_EXC_INFO_STACK 0
#else
  #define CYTHON_COMPILING_IN_PYPY 0
  #define CYTHON_COMPILING_IN_PYSTON 0
  #define CYTHON_COMPILING_IN_CPYTHON 1
  #ifndef CYTHON_USE_TYPE_SLOTS
    #define CYTHON_USE_TYPE_SLOTS 1
  #endif
  #if PY_VERSION_HEX < 0x02070000
    #undef CYTHON_USE_PYTYPE_LOOKUP
    #define CYTHON_USE_PYTYPE_LOOKUP 0
  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)
    #define CYTHON_USE_PYTYPE_LOOKUP 1
  #endif
  #if PY_MAJOR_VERSION < 3
    #undef CYTHON_USE_ASYNC_SLOTS
    #define CYTHON_USE_ASYNC_SLOTS 0
  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
    #define CYTHON_USE_ASYNC_SLOTS 1
  #endif
  #if PY_VERSION_HEX < 0x02070000
    #undef CYTHON_USE_PYLONG_INTERNALS
    #define CYTHON_USE_PYLONG_INTERNALS 0
  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)
    #define CYTHON_USE_PYLONG_INTERNALS 1
  #endif
  #ifndef CYTHON_USE_PYLIST_INTERNALS
    #define CYTHON_USE_PYLIST_INTERNALS 1
  #endif
  #ifndef CYTHON_USE_UNICODE_INTERNALS
    #define CYTHON_USE_UNICODE_INTERNALS 1
  #endif
  #if PY_VERSION_HEX < 0x030300F0
    #undef CYTHON_USE_UNICODE_WRITER
    #define CYTHON_USE_UNICODE_WRITER 0
  #elif !defined(CYTHON_USE_UNICODE_WRITER)
    #define CYTHON_USE_UNICODE_WRITER 1
  #endif
  #ifndef CYTHON_AVOID_BORROWED_REFS
    #define CYTHON_AVOID_BORROWED_REFS 0
  #endif
  #ifndef CYTHON_ASSUME_SAFE_MACROS
    #define CYTHON_ASSUME_SAFE_MACROS 1
  #endif
  #ifndef CYTHON_UNPACK_METHODS
    #define CYTHON_UNPACK_METHODS 1
  #endif
  #ifndef CYTHON_FAST_THREAD_STATE
    #define CYTHON_FAST_THREAD_STATE 1
  #endif
  #ifndef CYTHON_FAST_PYCALL
    #define CYTHON_FAST_PYCALL 1
  #endif
  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
    #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000)
  #endif
  #ifndef CYTHON_USE_TP_FINALIZE
    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
  #endif
  #ifndef CYTHON_USE_DICT_VERSIONS
    #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1)
  #endif
  #ifndef CYTHON_USE_EXC_INFO_STACK
    #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3)
  #endif
#endif
#if !defined(CYTHON_FAST_PYCCALL)
#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
#endif
#if CYTHON_USE_PYLONG_INTERNALS
  #include "longintrepr.h"
  #undef SHIFT
  #undef BASE
  #undef MASK
  #ifdef SIZEOF_VOID_P
    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
  #endif
#endif
#ifndef __has_attribute
  #define __has_attribute(x) 0
#endif
#ifndef __has_cpp_attribute
  #define __has_cpp_attribute(x) 0
#endif
#ifndef CYTHON_RESTRICT
  #if defined(__GNUC__)
    #define CYTHON_RESTRICT __restrict__
  #elif defined(_MSC_VER) && _MSC_VER >= 1400
    #define CYTHON_RESTRICT __restrict
  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define CYTHON_RESTRICT restrict
  #else
    #define CYTHON_RESTRICT
  #endif
#endif
#ifndef CYTHON_UNUSED
# if defined(__GNUC__)
#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
#     define CYTHON_UNUSED __attribute__ ((__unused__))
#   else
#     define CYTHON_UNUSED
#   endif
# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
#   define CYTHON_UNUSED __attribute__ ((__unused__))
# else
#   define CYTHON_UNUSED
# endif
#endif
#ifndef CYTHON_MAYBE_UNUSED_VAR
#  if defined(__cplusplus)
     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
#  else
#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
#  endif
#endif
#ifndef CYTHON_NCP_UNUSED
# if CYTHON_COMPILING_IN_CPYTHON
#  define CYTHON_NCP_UNUSED
# else
#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
# endif
#endif
#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
#ifdef _MSC_VER
    #ifndef _MSC_STDINT_H_
        #if _MSC_VER < 1300
           typedef unsigned char     uint8_t;
           typedef unsigned int      uint32_t;
        #else
           typedef unsigned __int8   uint8_t;
           typedef unsigned __int32  uint32_t;
        #endif
    #endif
#else
   #include <stdint.h>
#endif
#ifndef CYTHON_FALLTHROUGH
  #if defined(__cplusplus) && __cplusplus >= 201103L
    #if __has_cpp_attribute(fallthrough)
      #define CYTHON_FALLTHROUGH [[fallthrough]]
    #elif __has_cpp_attribute(clang::fallthrough)
      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
    #elif __has_cpp_attribute(gnu::fallthrough)
      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
    #endif
  #endif
  #ifndef CYTHON_FALLTHROUGH
    #if __has_attribute(fallthrough)
      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
    #else
      #define CYTHON_FALLTHROUGH
    #endif
  #endif
  #if defined(__clang__ ) && defined(__apple_build_version__)
    #if __apple_build_version__ < 7000000
      #undef  CYTHON_FALLTHROUGH
      #define CYTHON_FALLTHROUGH
    #endif
  #endif
#endif

#ifndef CYTHON_INLINE
  #if defined(__clang__)
    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
  #elif defined(__GNUC__)
    #define CYTHON_INLINE __inline__
  #elif defined(_MSC_VER)
    #define CYTHON_INLINE __inline
  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define CYTHON_INLINE inline
  #else
    #define CYTHON_INLINE
  #endif
#endif

#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
  #define Py_OptimizeFlag 0
#endif
#define __PYX_BUILD_PY_SSIZE_T "n"
#define CYTHON_FORMAT_SSIZE_T "z"
#if PY_MAJOR_VERSION < 3
  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
  #define __Pyx_DefaultClassType PyClass_Type
#else
  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
#if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2
  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
          PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
#else
  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
#endif
  #define __Pyx_DefaultClassType PyType_Type
#endif
#ifndef Py_TPFLAGS_CHECKTYPES
  #define Py_TPFLAGS_CHECKTYPES 0
#endif
#ifndef Py_TPFLAGS_HAVE_INDEX
  #define Py_TPFLAGS_HAVE_INDEX 0
#endif
#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
#endif
#ifndef Py_TPFLAGS_HAVE_FINALIZE
  #define Py_TPFLAGS_HAVE_FINALIZE 0
#endif
#ifndef METH_STACKLESS
  #define METH_STACKLESS 0
#endif
#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
  #ifndef METH_FASTCALL
     #define METH_FASTCALL 0x80
  #endif
  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
                                                          Py_ssize_t nargs, PyObject *kwnames);
#else
  #define __Pyx_PyCFunctionFast _PyCFunctionFast
  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
#endif
#if CYTHON_FAST_PYCCALL
#define __Pyx_PyFastCFunction_Check(func)\
    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))
#else
#define __Pyx_PyFastCFunction_Check(func) 0
#endif
#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
  #define PyObject_Malloc(s)   PyMem_Malloc(s)
  #define PyObject_Free(p)     PyMem_Free(p)
  #define PyObject_Realloc(p)  PyMem_Realloc(p)
#endif
#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1
  #define PyMem_RawMalloc(n)           PyMem_Malloc(n)
  #define PyMem_RawRealloc(p, n)       PyMem_Realloc(p, n)
  #define PyMem_RawFree(p)             PyMem_Free(p)
#endif
#if CYTHON_COMPILING_IN_PYSTON
  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
#else
  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
#endif
#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
  #define __Pyx_PyThreadState_Current PyThreadState_GET()
#elif PY_VERSION_HEX >= 0x03060000
  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
#elif PY_VERSION_HEX >= 0x03000000
  #define __Pyx_PyThreadState_Current PyThreadState_GET()
#else
  #define __Pyx_PyThreadState_Current _PyThreadState_Current
#endif
#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
#include "pythread.h"
#define Py_tss_NEEDS_INIT 0
typedef int Py_tss_t;
static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
  *key = PyThread_create_key();
  return 0;
}
static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
  *key = Py_tss_NEEDS_INIT;
  return key;
}
static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
  PyObject_Free(key);
}
static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
  return *key != Py_tss_NEEDS_INIT;
}
static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
  PyThread_delete_key(*key);
  *key = Py_tss_NEEDS_INIT;
}
static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
  return PyThread_set_key_value(*key, value);
}
static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
  return PyThread_get_key_value(*key);
}
#endif
#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
#else
#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
#endif
#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
#else
  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
#endif
#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
#else
#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)
#endif
#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
  #define CYTHON_PEP393_ENABLED 1
  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
                                              0 : _PyUnicode_Ready((PyObject *)(op)))
  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
  #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)
  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
  #else
  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))
  #endif
#else
  #define CYTHON_PEP393_ENABLED 0
  #define PyUnicode_1BYTE_KIND  1
  #define PyUnicode_2BYTE_KIND  2
  #define PyUnicode_4BYTE_KIND  4
  #define __Pyx_PyUnicode_READY(op)       (0)
  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
#endif
#if CYTHON_COMPILING_IN_PYPY
  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
#else
  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
#endif
#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
#endif
#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
#endif
#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
#endif
#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
#if PY_MAJOR_VERSION >= 3
  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
#else
  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
#endif
#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
  #define PyObject_ASCII(o)            PyObject_Repr(o)
#endif
#if PY_MAJOR_VERSION >= 3
  #define PyBaseString_Type            PyUnicode_Type
  #define PyStringObject               PyUnicodeObject
  #define PyString_Type                PyUnicode_Type
  #define PyString_Check               PyUnicode_Check
  #define PyString_CheckExact          PyUnicode_CheckExact
#ifndef PyObject_Unicode
  #define PyObject_Unicode             PyObject_Str
#endif
#endif
#if PY_MAJOR_VERSION >= 3
  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
#else
  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
#endif
#ifndef PySet_CheckExact
  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
#endif
#if PY_VERSION_HEX >= 0x030900A4
  #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)
  #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)
#else
  #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
  #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
#endif
#if CYTHON_ASSUME_SAFE_MACROS
  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)
#else
  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)
#endif
#if PY_MAJOR_VERSION >= 3
  #define PyIntObject                  PyLongObject
  #define PyInt_Type                   PyLong_Type
  #define PyInt_Check(op)              PyLong_Check(op)
  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
  #define PyInt_FromString             PyLong_FromString
  #define PyInt_FromUnicode            PyLong_FromUnicode
  #define PyInt_FromLong               PyLong_FromLong
  #define PyInt_FromSize_t             PyLong_FromSize_t
  #define PyInt_FromSsize_t            PyLong_FromSsize_t
  #define PyInt_AsLong                 PyLong_AsLong
  #define PyInt_AS_LONG                PyLong_AS_LONG
  #define PyInt_AsSsize_t              PyLong_AsSsize_t
  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
  #define PyNumber_Int                 PyNumber_Long
#endif
#if PY_MAJOR_VERSION >= 3
  #define PyBoolObject                 PyLongObject
#endif
#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
  #ifndef PyUnicode_InternFromString
    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
  #endif
#endif
#if PY_VERSION_HEX < 0x030200A4
  typedef long Py_hash_t;
  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
#else
  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
#endif
#if PY_MAJOR_VERSION >= 3
  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
#else
  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
#endif
#if CYTHON_USE_ASYNC_SLOTS
  #if PY_VERSION_HEX >= 0x030500B1
    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
  #else
    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
  #endif
#else
  #define __Pyx_PyType_AsAsync(obj) NULL
#endif
#ifndef __Pyx_PyAsyncMethodsStruct
    typedef struct {
        unaryfunc am_await;
        unaryfunc am_aiter;
        unaryfunc am_anext;
    } __Pyx_PyAsyncMethodsStruct;
#endif

#if defined(WIN32) || defined(MS_WINDOWS)
  #define _USE_MATH_DEFINES
#endif
#include <math.h>
#ifdef NAN
#define __PYX_NAN() ((float) NAN)
#else
static CYTHON_INLINE float __PYX_NAN() {
  float value;
  memset(&value, 0xFF, sizeof(value));
  return value;
}
#endif
#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
#define __Pyx_truncl trunc
#else
#define __Pyx_truncl truncl
#endif

#define __PYX_MARK_ERR_POS(f_index, lineno) \
    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
#define __PYX_ERR(f_index, lineno, Ln_error) \
    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }

#ifndef __PYX_EXTERN_C
  #ifdef __cplusplus
    #define __PYX_EXTERN_C extern "C"
  #else
    #define __PYX_EXTERN_C extern
  #endif
#endif

#define __PYX_HAVE__numpy__random___generator
#define __PYX_HAVE_API__numpy__random___generator
/* Early includes */
#include <string.h>
#include <stdio.h>
#include "pythread.h"
#include "numpy/arrayobject.h"
#include "numpy/ufuncobject.h"

    /* NumPy API declarations from "numpy/__init__.pxd" */
    
#include <stdint.h>
#include "numpy/random/bitgen.h"
#include "numpy/random/distributions.h"
#include "include/aligned_malloc.h"
#include <stdlib.h>
#include "pystate.h"
#ifdef _OPENMP
#include <omp.h>
#endif /* _OPENMP */

#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
#define CYTHON_WITHOUT_ASSERTIONS
#endif

typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;

#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
#define __PYX_DEFAULT_STRING_ENCODING ""
#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#define __Pyx_uchar_cast(c) ((unsigned char)c)
#define __Pyx_long_cast(x) ((long)x)
#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
    (sizeof(type) < sizeof(Py_ssize_t))  ||\
    (sizeof(type) > sizeof(Py_ssize_t) &&\
          likely(v < (type)PY_SSIZE_T_MAX ||\
                 v == (type)PY_SSIZE_T_MAX)  &&\
          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
                                v == (type)PY_SSIZE_T_MIN)))  ||\
    (sizeof(type) == sizeof(Py_ssize_t) &&\
          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
                               v == (type)PY_SSIZE_T_MAX)))  )
static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
    return (size_t) i < (size_t) limit;
}
#if defined (__cplusplus) && __cplusplus >= 201103L
    #include <cstdlib>
    #define __Pyx_sst_abs(value) std::abs(value)
#elif SIZEOF_INT >= SIZEOF_SIZE_T
    #define __Pyx_sst_abs(value) abs(value)
#elif SIZEOF_LONG >= SIZEOF_SIZE_T
    #define __Pyx_sst_abs(value) labs(value)
#elif defined (_MSC_VER)
    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define __Pyx_sst_abs(value) llabs(value)
#elif defined (__GNUC__)
    #define __Pyx_sst_abs(value) __builtin_llabs(value)
#else
    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
#endif
static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
#define __Pyx_PyBytes_FromString        PyBytes_FromString
#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
#if PY_MAJOR_VERSION < 3
    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#else
    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
#endif
#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
    const Py_UNICODE *u_end = u;
    while (*u_end++) ;
    return (size_t)(u_end - u - 1);
}
#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
#define __Pyx_PySequence_Tuple(obj)\
    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
#if CYTHON_ASSUME_SAFE_MACROS
#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
#else
#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
#endif
#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
#else
#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
#endif
#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
static int __Pyx_sys_getdefaultencoding_not_ascii;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
    PyObject* sys;
    PyObject* default_encoding = NULL;
    PyObject* ascii_chars_u = NULL;
    PyObject* ascii_chars_b = NULL;
    const char* default_encoding_c;
    sys = PyImport_ImportModule("sys");
    if (!sys) goto bad;
    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
    Py_DECREF(sys);
    if (!default_encoding) goto bad;
    default_encoding_c = PyBytes_AsString(default_encoding);
    if (!default_encoding_c) goto bad;
    if (strcmp(default_encoding_c, "ascii") == 0) {
        __Pyx_sys_getdefaultencoding_not_ascii = 0;
    } else {
        char ascii_chars[128];
        int c;
        for (c = 0; c < 128; c++) {
            ascii_chars[c] = c;
        }
        __Pyx_sys_getdefaultencoding_not_ascii = 1;
        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
        if (!ascii_chars_u) goto bad;
        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
            PyErr_Format(
                PyExc_ValueError,
                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
                default_encoding_c);
            goto bad;
        }
        Py_DECREF(ascii_chars_u);
        Py_DECREF(ascii_chars_b);
    }
    Py_DECREF(default_encoding);
    return 0;
bad:
    Py_XDECREF(default_encoding);
    Py_XDECREF(ascii_chars_u);
    Py_XDECREF(ascii_chars_b);
    return -1;
}
#endif
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
#else
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
static char* __PYX_DEFAULT_STRING_ENCODING;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
    PyObject* sys;
    PyObject* default_encoding = NULL;
    char* default_encoding_c;
    sys = PyImport_ImportModule("sys");
    if (!sys) goto bad;
    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
    Py_DECREF(sys);
    if (!default_encoding) goto bad;
    default_encoding_c = PyBytes_AsString(default_encoding);
    if (!default_encoding_c) goto bad;
    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
    Py_DECREF(default_encoding);
    return 0;
bad:
    Py_XDECREF(default_encoding);
    return -1;
}
#endif
#endif


/* Test for GCC > 2.95 */
#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
  #define likely(x)   __builtin_expect(!!(x), 1)
  #define unlikely(x) __builtin_expect(!!(x), 0)
#else /* !__GNUC__ or GCC < 2.95 */
  #define likely(x)   (x)
  #define unlikely(x) (x)
#endif /* __GNUC__ */
static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }

static PyObject *__pyx_m = NULL;
static PyObject *__pyx_d;
static PyObject *__pyx_b;
static PyObject *__pyx_cython_runtime = NULL;
static PyObject *__pyx_empty_tuple;
static PyObject *__pyx_empty_bytes;
static PyObject *__pyx_empty_unicode;
static int __pyx_lineno;
static int __pyx_clineno = 0;
static const char * __pyx_cfilenm= __FILE__;
static const char *__pyx_filename;

/* Header.proto */
#if !defined(CYTHON_CCOMPLEX)
  #if defined(__cplusplus)
    #define CYTHON_CCOMPLEX 1
  #elif defined(_Complex_I)
    #define CYTHON_CCOMPLEX 1
  #else
    #define CYTHON_CCOMPLEX 0
  #endif
#endif
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    #include <complex>
  #else
    #include <complex.h>
  #endif
#endif
#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
  #undef _Complex_I
  #define _Complex_I 1.0fj
#endif


static const char *__pyx_f[] = {
  "_generator.pyx",
  "__init__.pxd",
  "stringsource",
  "type.pxd",
  "bool.pxd",
  "complex.pxd",
  "bit_generator.pxd",
};
/* NoFastGil.proto */
#define __Pyx_PyGILState_Ensure PyGILState_Ensure
#define __Pyx_PyGILState_Release PyGILState_Release
#define __Pyx_FastGIL_Remember()
#define __Pyx_FastGIL_Forget()
#define __Pyx_FastGilFuncInit()

/* MemviewSliceStruct.proto */
struct __pyx_memoryview_obj;
typedef struct {
  struct __pyx_memoryview_obj *memview;
  char *data;
  Py_ssize_t shape[8];
  Py_ssize_t strides[8];
  Py_ssize_t suboffsets[8];
} __Pyx_memviewslice;
#define __Pyx_MemoryView_Len(m)  (m.shape[0])

/* Atomics.proto */
#include <pythread.h>
#ifndef CYTHON_ATOMICS
    #define CYTHON_ATOMICS 1
#endif
#define __pyx_atomic_int_type int
#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\
                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\
                    !defined(__i386__)
    #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1)
    #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1)
    #ifdef __PYX_DEBUG_ATOMICS
        #warning "Using GNU atomics"
    #endif
#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0
    #include <Windows.h>
    #undef __pyx_atomic_int_type
    #define __pyx_atomic_int_type LONG
    #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value)
    #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value)
    #ifdef __PYX_DEBUG_ATOMICS
        #pragma message ("Using MSVC atomics")
    #endif
#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0
    #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value)
    #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value)
    #ifdef __PYX_DEBUG_ATOMICS
        #warning "Using Intel atomics"
    #endif
#else
    #undef CYTHON_ATOMICS
    #define CYTHON_ATOMICS 0
    #ifdef __PYX_DEBUG_ATOMICS
        #warning "Not using atomics"
    #endif
#endif
typedef volatile __pyx_atomic_int_type __pyx_atomic_int;
#if CYTHON_ATOMICS
    #define __pyx_add_acquisition_count(memview)\
             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
    #define __pyx_sub_acquisition_count(memview)\
            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
#else
    #define __pyx_add_acquisition_count(memview)\
            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
    #define __pyx_sub_acquisition_count(memview)\
            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
#endif

/* ForceInitThreads.proto */
#ifndef __PYX_FORCE_INIT_THREADS
  #define __PYX_FORCE_INIT_THREADS 0
#endif

/* BufferFormatStructs.proto */
#define IS_UNSIGNED(type) (((type) -1) > 0)
struct __Pyx_StructField_;
#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
typedef struct {
  const char* name;
  struct __Pyx_StructField_* fields;
  size_t size;
  size_t arraysize[8];
  int ndim;
  char typegroup;
  char is_unsigned;
  int flags;
} __Pyx_TypeInfo;
typedef struct __Pyx_StructField_ {
  __Pyx_TypeInfo* type;
  const char* name;
  size_t offset;
} __Pyx_StructField;
typedef struct {
  __Pyx_StructField* field;
  size_t parent_offset;
} __Pyx_BufFmt_StackElem;
typedef struct {
  __Pyx_StructField root;
  __Pyx_BufFmt_StackElem* head;
  size_t fmt_offset;
  size_t new_count, enc_count;
  size_t struct_alignment;
  int is_complex;
  char enc_type;
  char new_packmode;
  char enc_packmode;
  char is_valid_array;
} __Pyx_BufFmt_Context;


/* "../__init__.pxd":689
 * # in Cython to enable them only on the right systems.
 * 
 * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
 * ctypedef npy_int16      int16_t
 * ctypedef npy_int32      int32_t
 */
typedef npy_int8 __pyx_t_5numpy_int8_t;

/* "../__init__.pxd":690
 * 
 * ctypedef npy_int8       int8_t
 * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
 * ctypedef npy_int32      int32_t
 * ctypedef npy_int64      int64_t
 */
typedef npy_int16 __pyx_t_5numpy_int16_t;

/* "../__init__.pxd":691
 * ctypedef npy_int8       int8_t
 * ctypedef npy_int16      int16_t
 * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
 * ctypedef npy_int64      int64_t
 * #ctypedef npy_int96      int96_t
 */
typedef npy_int32 __pyx_t_5numpy_int32_t;

/* "../__init__.pxd":692
 * ctypedef npy_int16      int16_t
 * ctypedef npy_int32      int32_t
 * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
 * #ctypedef npy_int96      int96_t
 * #ctypedef npy_int128     int128_t
 */
typedef npy_int64 __pyx_t_5numpy_int64_t;

/* "../__init__.pxd":696
 * #ctypedef npy_int128     int128_t
 * 
 * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uint16     uint16_t
 * ctypedef npy_uint32     uint32_t
 */
typedef npy_uint8 __pyx_t_5numpy_uint8_t;

/* "../__init__.pxd":697
 * 
 * ctypedef npy_uint8      uint8_t
 * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uint32     uint32_t
 * ctypedef npy_uint64     uint64_t
 */
typedef npy_uint16 __pyx_t_5numpy_uint16_t;

/* "../__init__.pxd":698
 * ctypedef npy_uint8      uint8_t
 * ctypedef npy_uint16     uint16_t
 * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uint64     uint64_t
 * #ctypedef npy_uint96     uint96_t
 */
typedef npy_uint32 __pyx_t_5numpy_uint32_t;

/* "../__init__.pxd":699
 * ctypedef npy_uint16     uint16_t
 * ctypedef npy_uint32     uint32_t
 * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
 * #ctypedef npy_uint96     uint96_t
 * #ctypedef npy_uint128    uint128_t
 */
typedef npy_uint64 __pyx_t_5numpy_uint64_t;

/* "../__init__.pxd":703
 * #ctypedef npy_uint128    uint128_t
 * 
 * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
 * ctypedef npy_float64    float64_t
 * #ctypedef npy_float80    float80_t
 */
typedef npy_float32 __pyx_t_5numpy_float32_t;

/* "../__init__.pxd":704
 * 
 * ctypedef npy_float32    float32_t
 * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
 * #ctypedef npy_float80    float80_t
 * #ctypedef npy_float128   float128_t
 */
typedef npy_float64 __pyx_t_5numpy_float64_t;

/* "../__init__.pxd":713
 * # The int types are mapped a bit surprising --
 * # numpy.int corresponds to 'l' and numpy.long to 'q'
 * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
 * ctypedef npy_longlong   long_t
 * ctypedef npy_longlong   longlong_t
 */
typedef npy_long __pyx_t_5numpy_int_t;

/* "../__init__.pxd":714
 * # numpy.int corresponds to 'l' and numpy.long to 'q'
 * ctypedef npy_long       int_t
 * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
 * ctypedef npy_longlong   longlong_t
 * 
 */
typedef npy_longlong __pyx_t_5numpy_long_t;

/* "../__init__.pxd":715
 * ctypedef npy_long       int_t
 * ctypedef npy_longlong   long_t
 * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_ulong      uint_t
 */
typedef npy_longlong __pyx_t_5numpy_longlong_t;

/* "../__init__.pxd":717
 * ctypedef npy_longlong   longlong_t
 * 
 * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
 * ctypedef npy_ulonglong  ulong_t
 * ctypedef npy_ulonglong  ulonglong_t
 */
typedef npy_ulong __pyx_t_5numpy_uint_t;

/* "../__init__.pxd":718
 * 
 * ctypedef npy_ulong      uint_t
 * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
 * ctypedef npy_ulonglong  ulonglong_t
 * 
 */
typedef npy_ulonglong __pyx_t_5numpy_ulong_t;

/* "../__init__.pxd":719
 * ctypedef npy_ulong      uint_t
 * ctypedef npy_ulonglong  ulong_t
 * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_intp       intp_t
 */
typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;

/* "../__init__.pxd":721
 * ctypedef npy_ulonglong  ulonglong_t
 * 
 * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uintp      uintp_t
 * 
 */
typedef npy_intp __pyx_t_5numpy_intp_t;

/* "../__init__.pxd":722
 * 
 * ctypedef npy_intp       intp_t
 * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_double     float_t
 */
typedef npy_uintp __pyx_t_5numpy_uintp_t;

/* "../__init__.pxd":724
 * ctypedef npy_uintp      uintp_t
 * 
 * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
 * ctypedef npy_double     double_t
 * ctypedef npy_longdouble longdouble_t
 */
typedef npy_double __pyx_t_5numpy_float_t;

/* "../__init__.pxd":725
 * 
 * ctypedef npy_double     float_t
 * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
 * ctypedef npy_longdouble longdouble_t
 * 
 */
typedef npy_double __pyx_t_5numpy_double_t;

/* "../__init__.pxd":726
 * ctypedef npy_double     float_t
 * ctypedef npy_double     double_t
 * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_cfloat      cfloat_t
 */
typedef npy_longdouble __pyx_t_5numpy_longdouble_t;

/* "_bounded_integers.pxd":5
 * import numpy as np
 * cimport numpy as np
 * ctypedef np.npy_bool bool_t             # <<<<<<<<<<<<<<
 * 
 * from numpy.random cimport bitgen_t
 */
typedef npy_bool __pyx_t_5numpy_6random_17_bounded_integers_bool_t;
/* Declarations.proto */
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    typedef ::std::complex< float > __pyx_t_float_complex;
  #else
    typedef float _Complex __pyx_t_float_complex;
  #endif
#else
    typedef struct { float real, imag; } __pyx_t_float_complex;
#endif
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);

/* Declarations.proto */
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    typedef ::std::complex< double > __pyx_t_double_complex;
  #else
    typedef double _Complex __pyx_t_double_complex;
  #endif
#else
    typedef struct { double real, imag; } __pyx_t_double_complex;
#endif
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);


/*--- Type declarations ---*/
struct __pyx_obj_5numpy_6random_13bit_generator_BitGenerator;
struct __pyx_obj_5numpy_6random_13bit_generator_SeedSequence;
struct __pyx_obj_5numpy_6random_13bit_generator_SeedlessSequence;
struct __pyx_obj_5numpy_6random_10_generator_Generator;
struct __pyx_array_obj;
struct __pyx_MemviewEnum_obj;
struct __pyx_memoryview_obj;
struct __pyx_memoryviewslice_obj;

/* "../__init__.pxd":728
 * ctypedef npy_longdouble longdouble_t
 * 
 * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
 * ctypedef npy_cdouble     cdouble_t
 * ctypedef npy_clongdouble clongdouble_t
 */
typedef npy_cfloat __pyx_t_5numpy_cfloat_t;

/* "../__init__.pxd":729
 * 
 * ctypedef npy_cfloat      cfloat_t
 * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
 * ctypedef npy_clongdouble clongdouble_t
 * 
 */
typedef npy_cdouble __pyx_t_5numpy_cdouble_t;

/* "../__init__.pxd":730
 * ctypedef npy_cfloat      cfloat_t
 * ctypedef npy_cdouble     cdouble_t
 * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_cdouble     complex_t
 */
typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;

/* "../__init__.pxd":732
 * ctypedef npy_clongdouble clongdouble_t
 * 
 * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
 * 
 * cdef inline object PyArray_MultiIterNew1(a):
 */
typedef npy_cdouble __pyx_t_5numpy_complex_t;

/* "_common.pxd":14
 * cdef uint64_t MAXSIZE
 * 
 * cdef enum ConstraintType:             # <<<<<<<<<<<<<<
 *     CONS_NONE
 *     CONS_NON_NEGATIVE
 */
enum __pyx_t_5numpy_6random_7_common_ConstraintType {
  __pyx_e_5numpy_6random_7_common_CONS_NONE,
  __pyx_e_5numpy_6random_7_common_CONS_NON_NEGATIVE,
  __pyx_e_5numpy_6random_7_common_CONS_POSITIVE,
  __pyx_e_5numpy_6random_7_common_CONS_POSITIVE_NOT_NAN,
  __pyx_e_5numpy_6random_7_common_CONS_BOUNDED_0_1,
  __pyx_e_5numpy_6random_7_common_CONS_BOUNDED_0_1_NOTNAN,
  __pyx_e_5numpy_6random_7_common_CONS_BOUNDED_GT_0_1,
  __pyx_e_5numpy_6random_7_common_CONS_GT_1,
  __pyx_e_5numpy_6random_7_common_CONS_GTE_1,
  __pyx_e_5numpy_6random_7_common_CONS_POISSON,
  __pyx_e_5numpy_6random_7_common_LEGACY_CONS_POISSON
};

/* "_common.pxd":27
 *     LEGACY_CONS_POISSON
 * 
 * ctypedef ConstraintType constraint_type             # <<<<<<<<<<<<<<
 * 
 * cdef object benchmark(bitgen_t *bitgen, object lock, Py_ssize_t cnt, object method)
 */
typedef enum __pyx_t_5numpy_6random_7_common_ConstraintType __pyx_t_5numpy_6random_7_common_constraint_type;

/* "_common.pxd":42
 *     cdef void PyArray_free_aligned(void *p)
 * 
 * ctypedef double (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (*random_double_0)(void *state) nogil
 * ctypedef double (*random_double_1)(void *state, double a) nogil
 */
typedef double (*__pyx_t_5numpy_6random_7_common_random_double_fill)(bitgen_t *, npy_intp, double *);

/* "_common.pxd":43
 * 
 * ctypedef double (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out) nogil
 * ctypedef double (*random_double_0)(void *state) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (*random_double_1)(void *state, double a) nogil
 * ctypedef double (*random_double_2)(void *state, double a, double b) nogil
 */
typedef double (*__pyx_t_5numpy_6random_7_common_random_double_0)(void *);

/* "_common.pxd":44
 * ctypedef double (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out) nogil
 * ctypedef double (*random_double_0)(void *state) nogil
 * ctypedef double (*random_double_1)(void *state, double a) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (*random_double_2)(void *state, double a, double b) nogil
 * ctypedef double (*random_double_3)(void *state, double a, double b, double c) nogil
 */
typedef double (*__pyx_t_5numpy_6random_7_common_random_double_1)(void *, double);

/* "_common.pxd":45
 * ctypedef double (*random_double_0)(void *state) nogil
 * ctypedef double (*random_double_1)(void *state, double a) nogil
 * ctypedef double (*random_double_2)(void *state, double a, double b) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (*random_double_3)(void *state, double a, double b, double c) nogil
 * 
 */
typedef double (*__pyx_t_5numpy_6random_7_common_random_double_2)(void *, double, double);

/* "_common.pxd":46
 * ctypedef double (*random_double_1)(void *state, double a) nogil
 * ctypedef double (*random_double_2)(void *state, double a, double b) nogil
 * ctypedef double (*random_double_3)(void *state, double a, double b, double c) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef double (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out) nogil
 */
typedef double (*__pyx_t_5numpy_6random_7_common_random_double_3)(void *, double, double, double);

/* "_common.pxd":48
 * ctypedef double (*random_double_3)(void *state, double a, double b, double c) nogil
 * 
 * ctypedef double (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out) nogil             # <<<<<<<<<<<<<<
 * ctypedef float (*random_float_0)(bitgen_t *state) nogil
 * ctypedef float (*random_float_1)(bitgen_t *state, float a) nogil
 */
typedef double (*__pyx_t_5numpy_6random_7_common_random_float_fill)(bitgen_t *, npy_intp, float *);

/* "_common.pxd":49
 * 
 * ctypedef double (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out) nogil
 * ctypedef float (*random_float_0)(bitgen_t *state) nogil             # <<<<<<<<<<<<<<
 * ctypedef float (*random_float_1)(bitgen_t *state, float a) nogil
 * 
 */
typedef float (*__pyx_t_5numpy_6random_7_common_random_float_0)(bitgen_t *);

/* "_common.pxd":50
 * ctypedef double (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out) nogil
 * ctypedef float (*random_float_0)(bitgen_t *state) nogil
 * ctypedef float (*random_float_1)(bitgen_t *state, float a) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef int64_t (*random_uint_0)(void *state) nogil
 */
typedef float (*__pyx_t_5numpy_6random_7_common_random_float_1)(bitgen_t *, float);

/* "_common.pxd":52
 * ctypedef float (*random_float_1)(bitgen_t *state, float a) nogil
 * 
 * ctypedef int64_t (*random_uint_0)(void *state) nogil             # <<<<<<<<<<<<<<
 * ctypedef int64_t (*random_uint_d)(void *state, double a) nogil
 * ctypedef int64_t (*random_uint_dd)(void *state, double a, double b) nogil
 */
typedef int64_t (*__pyx_t_5numpy_6random_7_common_random_uint_0)(void *);

/* "_common.pxd":53
 * 
 * ctypedef int64_t (*random_uint_0)(void *state) nogil
 * ctypedef int64_t (*random_uint_d)(void *state, double a) nogil             # <<<<<<<<<<<<<<
 * ctypedef int64_t (*random_uint_dd)(void *state, double a, double b) nogil
 * ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b) nogil
 */
typedef int64_t (*__pyx_t_5numpy_6random_7_common_random_uint_d)(void *, double);

/* "_common.pxd":54
 * ctypedef int64_t (*random_uint_0)(void *state) nogil
 * ctypedef int64_t (*random_uint_d)(void *state, double a) nogil
 * ctypedef int64_t (*random_uint_dd)(void *state, double a, double b) nogil             # <<<<<<<<<<<<<<
 * ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b) nogil
 * ctypedef int64_t (*random_uint_i)(void *state, int64_t a) nogil
 */
typedef int64_t (*__pyx_t_5numpy_6random_7_common_random_uint_dd)(void *, double, double);

/* "_common.pxd":55
 * ctypedef int64_t (*random_uint_d)(void *state, double a) nogil
 * ctypedef int64_t (*random_uint_dd)(void *state, double a, double b) nogil
 * ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b) nogil             # <<<<<<<<<<<<<<
 * ctypedef int64_t (*random_uint_i)(void *state, int64_t a) nogil
 * ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c) nogil
 */
typedef int64_t (*__pyx_t_5numpy_6random_7_common_random_uint_di)(void *, double, uint64_t);

/* "_common.pxd":56
 * ctypedef int64_t (*random_uint_dd)(void *state, double a, double b) nogil
 * ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b) nogil
 * ctypedef int64_t (*random_uint_i)(void *state, int64_t a) nogil             # <<<<<<<<<<<<<<
 * ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c) nogil
 * 
 */
typedef int64_t (*__pyx_t_5numpy_6random_7_common_random_uint_i)(void *, int64_t);

/* "_common.pxd":57
 * ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b) nogil
 * ctypedef int64_t (*random_uint_i)(void *state, int64_t a) nogil
 * ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state) nogil
 */
typedef int64_t (*__pyx_t_5numpy_6random_7_common_random_uint_iii)(void *, int64_t, int64_t, int64_t);

/* "_common.pxd":59
 * ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c) nogil
 * 
 * ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state) nogil             # <<<<<<<<<<<<<<
 * ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a) nogil
 * 
 */
typedef uint32_t (*__pyx_t_5numpy_6random_7_common_random_uint_0_32)(bitgen_t *);

/* "_common.pxd":60
 * 
 * ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state) nogil
 * ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b) nogil
 */
typedef uint32_t (*__pyx_t_5numpy_6random_7_common_random_uint_1_i_32)(bitgen_t *, uint32_t);

/* "_common.pxd":62
 * ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a) nogil
 * 
 * ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b) nogil             # <<<<<<<<<<<<<<
 * ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b) nogil
 * 
 */
typedef int32_t (*__pyx_t_5numpy_6random_7_common_random_int_2_i_32)(bitgen_t *, int32_t, int32_t);

/* "_common.pxd":63
 * 
 * ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b) nogil
 * ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b) nogil             # <<<<<<<<<<<<<<
 * 
 * cdef double kahan_sum(double *darr, np.npy_intp n)
 */
typedef int64_t (*__pyx_t_5numpy_6random_7_common_random_int_2_i)(bitgen_t *, int64_t, int64_t);

/* "numpy/random/bit_generator.pxd":14
 *     ctypedef bitgen bitgen_t
 * 
 * cdef class BitGenerator():             # <<<<<<<<<<<<<<
 *     cdef readonly object _seed_seq
 *     cdef readonly object lock
 */
struct __pyx_obj_5numpy_6random_13bit_generator_BitGenerator {
  PyObject_HEAD
  PyObject *_seed_seq;
  PyObject *lock;
  bitgen_t _bitgen;
  PyObject *_ctypes;
  PyObject *_cffi;
  PyObject *capsule;
};


/* "numpy/random/bit_generator.pxd":23
 * 
 * 
 * cdef class SeedSequence():             # <<<<<<<<<<<<<<
 *     cdef readonly object entropy
 *     cdef readonly tuple spawn_key
 */
struct __pyx_obj_5numpy_6random_13bit_generator_SeedSequence {
  PyObject_HEAD
  struct __pyx_vtabstruct_5numpy_6random_13bit_generator_SeedSequence *__pyx_vtab;
  PyObject *entropy;
  PyObject *spawn_key;
  uint32_t pool_size;
  PyObject *pool;
  uint32_t n_children_spawned;
};


/* "numpy/random/bit_generator.pxd":34
 *     cdef get_assembled_entropy(self)
 * 
 * cdef class SeedlessSequence():             # <<<<<<<<<<<<<<
 *     pass
 */
struct __pyx_obj_5numpy_6random_13bit_generator_SeedlessSequence {
  PyObject_HEAD
};


/* "numpy/random/_generator.pyx":60
 * 
 * 
 * cdef class Generator:             # <<<<<<<<<<<<<<
 *     """
 *     Generator(bit_generator)
 */
struct __pyx_obj_5numpy_6random_10_generator_Generator {
  PyObject_HEAD
  struct __pyx_vtabstruct_5numpy_6random_10_generator_Generator *__pyx_vtab;
  PyObject *_bit_generator;
  bitgen_t _bitgen;
  binomial_t _binomial;
  PyObject *lock;
};


/* "View.MemoryView":105
 * 
 * @cname("__pyx_array")
 * cdef class array:             # <<<<<<<<<<<<<<
 * 
 *     cdef:
 */
struct __pyx_array_obj {
  PyObject_HEAD
  struct __pyx_vtabstruct_array *__pyx_vtab;
  char *data;
  Py_ssize_t len;
  char *format;
  int ndim;
  Py_ssize_t *_shape;
  Py_ssize_t *_strides;
  Py_ssize_t itemsize;
  PyObject *mode;
  PyObject *_format;
  void (*callback_free_data)(void *);
  int free_data;
  int dtype_is_object;
};


/* "View.MemoryView":279
 * 
 * @cname('__pyx_MemviewEnum')
 * cdef class Enum(object):             # <<<<<<<<<<<<<<
 *     cdef object name
 *     def __init__(self, name):
 */
struct __pyx_MemviewEnum_obj {
  PyObject_HEAD
  PyObject *name;
};


/* "View.MemoryView":330
 * 
 * @cname('__pyx_memoryview')
 * cdef class memoryview(object):             # <<<<<<<<<<<<<<
 * 
 *     cdef object obj
 */
struct __pyx_memoryview_obj {
  PyObject_HEAD
  struct __pyx_vtabstruct_memoryview *__pyx_vtab;
  PyObject *obj;
  PyObject *_size;
  PyObject *_array_interface;
  PyThread_type_lock lock;
  __pyx_atomic_int acquisition_count[2];
  __pyx_atomic_int *acquisition_count_aligned_p;
  Py_buffer view;
  int flags;
  int dtype_is_object;
  __Pyx_TypeInfo *typeinfo;
};


/* "View.MemoryView":965
 * 
 * @cname('__pyx_memoryviewslice')
 * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
 *     "Internal class for passing memoryview slices to Python"
 * 
 */
struct __pyx_memoryviewslice_obj {
  struct __pyx_memoryview_obj __pyx_base;
  __Pyx_memviewslice from_slice;
  PyObject *from_object;
  PyObject *(*to_object_func)(char *);
  int (*to_dtype_func)(char *, PyObject *);
};



/* "numpy/random/bit_generator.pxd":23
 * 
 * 
 * cdef class SeedSequence():             # <<<<<<<<<<<<<<
 *     cdef readonly object entropy
 *     cdef readonly tuple spawn_key
 */

struct __pyx_vtabstruct_5numpy_6random_13bit_generator_SeedSequence {
  PyObject *(*mix_entropy)(struct __pyx_obj_5numpy_6random_13bit_generator_SeedSequence *, PyArrayObject *, PyArrayObject *);
  PyObject *(*get_assembled_entropy)(struct __pyx_obj_5numpy_6random_13bit_generator_SeedSequence *);
};
static struct __pyx_vtabstruct_5numpy_6random_13bit_generator_SeedSequence *__pyx_vtabptr_5numpy_6random_13bit_generator_SeedSequence;


/* "numpy/random/_generator.pyx":60
 * 
 * 
 * cdef class Generator:             # <<<<<<<<<<<<<<
 *     """
 *     Generator(bit_generator)
 */

struct __pyx_vtabstruct_5numpy_6random_10_generator_Generator {
  PyObject *(*_shuffle_raw)(struct __pyx_obj_5numpy_6random_10_generator_Generator *, npy_intp, npy_intp, npy_intp, npy_intp, char *, char *);
  void (*_shuffle_int)(struct __pyx_obj_5numpy_6random_10_generator_Generator *, npy_intp, npy_intp, int64_t *);
};
static struct __pyx_vtabstruct_5numpy_6random_10_generator_Generator *__pyx_vtabptr_5numpy_6random_10_generator_Generator;
static CYTHON_INLINE PyObject *__pyx_f_5numpy_6random_10_generator_9Generator__shuffle_raw(struct __pyx_obj_5numpy_6random_10_generator_Generator *, npy_intp, npy_intp, npy_intp, npy_intp, char *, char *);
static CYTHON_INLINE void __pyx_f_5numpy_6random_10_generator_9Generator__shuffle_int(struct __pyx_obj_5numpy_6random_10_generator_Generator *, npy_intp, npy_intp, int64_t *);


/* "View.MemoryView":105
 * 
 * @cname("__pyx_array")
 * cdef class array:             # <<<<<<<<<<<<<<
 * 
 *     cdef:
 */

struct __pyx_vtabstruct_array {
  PyObject *(*get_memview)(struct __pyx_array_obj *);
};
static struct __pyx_vtabstruct_array *__pyx_vtabptr_array;


/* "View.MemoryView":330
 * 
 * @cname('__pyx_memoryview')
 * cdef class memoryview(object):             # <<<<<<<<<<<<<<
 * 
 *     cdef object obj
 */

struct __pyx_vtabstruct_memoryview {
  char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *);
  PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *);
  PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
  PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *);
  PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
  PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *);
  PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *);
};
static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview;


/* "View.MemoryView":965
 * 
 * @cname('__pyx_memoryviewslice')
 * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
 *     "Internal class for passing memoryview slices to Python"
 * 
 */

struct __pyx_vtabstruct__memoryviewslice {
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};
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#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__)
static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int);
static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int);

/* ListAppend.proto */
#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
    PyListObject* L = (PyListObject*) list;
    Py_ssize_t len = Py_SIZE(list);
    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
        Py_INCREF(x);
        PyList_SET_ITEM(list, len, x);
        __Pyx_SET_SIZE(list, len + 1);
        return 0;
    }
    return PyList_Append(list, x);
}
#else
#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
#endif

/* None.proto */
static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);

/* py_abs.proto */
#if CYTHON_USE_PYLONG_INTERNALS
static PyObject *__Pyx_PyLong_AbsNeg(PyObject *num);
#define __Pyx_PyNumber_Absolute(x)\
    ((likely(PyLong_CheckExact(x))) ?\
         (likely(Py_SIZE(x) >= 0) ? (Py_INCREF(x), (x)) : __Pyx_PyLong_AbsNeg(x)) :\
         PyNumber_Absolute(x))
#else
#define __Pyx_PyNumber_Absolute(x)  PyNumber_Absolute(x)
#endif

/* MatrixMultiply.proto */
#if PY_VERSION_HEX >= 0x03050000
  #define __Pyx_PyNumber_MatrixMultiply(x,y)         PyNumber_MatrixMultiply(x,y)
  #define __Pyx_PyNumber_InPlaceMatrixMultiply(x,y)  PyNumber_InPlaceMatrixMultiply(x,y)
#else
#define __Pyx_PyNumber_MatrixMultiply(x,y)         __Pyx__PyNumber_MatrixMultiply(x, y, "@")
static PyObject* __Pyx__PyNumber_MatrixMultiply(PyObject* x, PyObject* y, const char* op_name);
static PyObject* __Pyx_PyNumber_InPlaceMatrixMultiply(PyObject* x, PyObject* y);
#endif

/* SetItemInt.proto */
#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
    __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\
    (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\
               __Pyx_SetItemInt_Generic(o, to_py_func(i), v)))
static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v);
static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
                                               int is_list, int wraparound, int boundscheck);

/* ArgTypeTest.proto */
#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
    ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\
        __Pyx__ArgTypeTest(obj, type, name, exact))
static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);

/* StrEquals.proto */
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
#else
#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
#endif

/* UnaryNegOverflows.proto */
#define UNARY_NEG_WOULD_OVERFLOW(x)\
        (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x)))

static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/
/* decode_c_string_utf16.proto */
static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) {
    int byteorder = 0;
    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
}
static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) {
    int byteorder = -1;
    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
}
static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) {
    int byteorder = 1;
    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
}

/* decode_c_string.proto */
static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
         const char* encoding, const char* errors,
         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));

/* GetAttr3.proto */
static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);

/* RaiseNoneIterError.proto */
static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);

/* SwapException.proto */
#if CYTHON_FAST_THREAD_STATE
#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
#else
static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
#endif

/* FastTypeChecks.proto */
#if CYTHON_COMPILING_IN_CPYTHON
#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
#else
#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
#endif
#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)

static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
/* ListCompAppend.proto */
#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
    PyListObject* L = (PyListObject*) list;
    Py_ssize_t len = Py_SIZE(list);
    if (likely(L->allocated > len)) {
        Py_INCREF(x);
        PyList_SET_ITEM(list, len, x);
        __Pyx_SET_SIZE(list, len + 1);
        return 0;
    }
    return PyList_Append(list, x);
}
#else
#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
#endif

/* PyIntBinop.proto */
#if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
#else
#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
#endif

/* ListExtend.proto */
static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
#if CYTHON_COMPILING_IN_CPYTHON
    PyObject* none = _PyList_Extend((PyListObject*)L, v);
    if (unlikely(!none))
        return -1;
    Py_DECREF(none);
    return 0;
#else
    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
#endif
}

/* PyObject_GenericGetAttrNoDict.proto */
#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
#else
#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
#endif

/* PyObject_GenericGetAttr.proto */
#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
#else
#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
#endif

/* SetVTable.proto */
static int __Pyx_SetVtable(PyObject *dict, void *vtable);

/* PyObjectGetAttrStrNoError.proto */
static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name);

/* SetupReduce.proto */
static int __Pyx_setup_reduce(PyObject* type_obj);

/* TypeImport.proto */
#ifndef __PYX_HAVE_RT_ImportType_proto
#define __PYX_HAVE_RT_ImportType_proto
enum __Pyx_ImportType_CheckSize {
   __Pyx_ImportType_CheckSize_Error = 0,
   __Pyx_ImportType_CheckSize_Warn = 1,
   __Pyx_ImportType_CheckSize_Ignore = 2
};
static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size);
#endif

/* GetVTable.proto */
static void* __Pyx_GetVtable(PyObject *dict);

/* CLineInTraceback.proto */
#ifdef CYTHON_CLINE_IN_TRACEBACK
#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
#else
static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
#endif

/* CodeObjectCache.proto */
typedef struct {
    PyCodeObject* code_object;
    int code_line;
} __Pyx_CodeObjectCacheEntry;
struct __Pyx_CodeObjectCache {
    int count;
    int max_count;
    __Pyx_CodeObjectCacheEntry* entries;
};
static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
static PyCodeObject *__pyx_find_code_object(int code_line);
static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);

/* AddTraceback.proto */
static void __Pyx_AddTraceback(const char *funcname, int c_line,
                               int py_line, const char *filename);

#if PY_MAJOR_VERSION < 3
    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
    static void __Pyx_ReleaseBuffer(Py_buffer *view);
#else
    #define __Pyx_GetBuffer PyObject_GetBuffer
    #define __Pyx_ReleaseBuffer PyBuffer_Release
#endif


/* BufferStructDeclare.proto */
typedef struct {
  Py_ssize_t shape, strides, suboffsets;
} __Pyx_Buf_DimInfo;
typedef struct {
  size_t refcount;
  Py_buffer pybuffer;
} __Pyx_Buffer;
typedef struct {
  __Pyx_Buffer *rcbuffer;
  char *data;
  __Pyx_Buf_DimInfo diminfo[8];
} __Pyx_LocalBuf_ND;

/* MemviewSliceIsContig.proto */
static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);

/* OverlappingSlices.proto */
static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
                                __Pyx_memviewslice *slice2,
                                int ndim, size_t itemsize);

/* Capsule.proto */
static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int64_t(int64_t value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint64_t(uint64_t value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value);

/* RealImag.proto */
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    #define __Pyx_CREAL(z) ((z).real())
    #define __Pyx_CIMAG(z) ((z).imag())
  #else
    #define __Pyx_CREAL(z) (__real__(z))
    #define __Pyx_CIMAG(z) (__imag__(z))
  #endif
#else
    #define __Pyx_CREAL(z) ((z).real)
    #define __Pyx_CIMAG(z) ((z).imag)
#endif
#if defined(__cplusplus) && CYTHON_CCOMPLEX\
        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
#else
    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
#endif

/* Arithmetic.proto */
#if CYTHON_CCOMPLEX
    #define __Pyx_c_eq_float(a, b)   ((a)==(b))
    #define __Pyx_c_sum_float(a, b)  ((a)+(b))
    #define __Pyx_c_diff_float(a, b) ((a)-(b))
    #define __Pyx_c_prod_float(a, b) ((a)*(b))
    #define __Pyx_c_quot_float(a, b) ((a)/(b))
    #define __Pyx_c_neg_float(a)     (-(a))
  #ifdef __cplusplus
    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)
    #define __Pyx_c_conj_float(z)    (::std::conj(z))
    #if 1
        #define __Pyx_c_abs_float(z)     (::std::abs(z))
        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))
    #endif
  #else
    #define __Pyx_c_is_zero_float(z) ((z)==0)
    #define __Pyx_c_conj_float(z)    (conjf(z))
    #if 1
        #define __Pyx_c_abs_float(z)     (cabsf(z))
        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))
    #endif
 #endif
#else
    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);
    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);
    #if 1
        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);
        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);
    #endif
#endif

/* Arithmetic.proto */
#if CYTHON_CCOMPLEX
    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
    #define __Pyx_c_diff_double(a, b) ((a)-(b))
    #define __Pyx_c_prod_double(a, b) ((a)*(b))
    #define __Pyx_c_quot_double(a, b) ((a)/(b))
    #define __Pyx_c_neg_double(a)     (-(a))
  #ifdef __cplusplus
    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
    #define __Pyx_c_conj_double(z)    (::std::conj(z))
    #if 1
        #define __Pyx_c_abs_double(z)     (::std::abs(z))
        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
    #endif
  #else
    #define __Pyx_c_is_zero_double(z) ((z)==0)
    #define __Pyx_c_conj_double(z)    (conj(z))
    #if 1
        #define __Pyx_c_abs_double(z)     (cabs(z))
        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
    #endif
 #endif
#else
    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
    #if 1
        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
    #endif
#endif

/* MemviewSliceCopyTemplate.proto */
static __Pyx_memviewslice
__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
                                 const char *mode, int ndim,
                                 size_t sizeof_dtype, int contig_flag,
                                 int dtype_is_object);

/* CIntFromPy.proto */
static CYTHON_INLINE Py_intptr_t __Pyx_PyInt_As_Py_intptr_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE int64_t __Pyx_PyInt_As_int64_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);

/* CIntFromPy.proto */
static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);

/* IsLittleEndian.proto */
static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);

/* BufferFormatCheck.proto */
static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
                              __Pyx_BufFmt_StackElem* stack,
                              __Pyx_TypeInfo* type);

/* TypeInfoCompare.proto */
static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);

/* MemviewSliceValidateAndInit.proto */
static int __Pyx_ValidateAndInit_memviewslice(
                int *axes_specs,
                int c_or_f_flag,
                int buf_flags,
                int ndim,
                __Pyx_TypeInfo *dtype,
                __Pyx_BufFmt_StackElem stack[],
                __Pyx_memviewslice *memviewslice,
                PyObject *original_obj);

/* ObjectToMemviewSlice.proto */
static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_nn_uint64_t(PyObject *, int writable_flag);

/* CheckBinaryVersion.proto */
static int __Pyx_check_binary_version(void);

/* VoidPtrImport.proto */
static int __Pyx_ImportVoidPtr(PyObject *module, const char *name, void **p, const char *sig);

/* FunctionImport.proto */
static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig);

/* InitStrings.proto */
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);

static CYTHON_INLINE PyObject *__pyx_f_5numpy_6random_10_generator_9Generator__shuffle_raw(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, npy_intp __pyx_v_n, npy_intp __pyx_v_first, npy_intp __pyx_v_itemsize, npy_intp __pyx_v_stride, char *__pyx_v_data, char *__pyx_v_buf); /* proto*/
static CYTHON_INLINE void __pyx_f_5numpy_6random_10_generator_9Generator__shuffle_int(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, npy_intp __pyx_v_n, npy_intp __pyx_v_first, int64_t *__pyx_v_data); /* proto*/
static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/

/* Module declarations from 'cpython.pycapsule' */

/* Module declarations from 'cpython.version' */

/* Module declarations from '__builtin__' */

/* Module declarations from 'cpython.type' */
static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;

/* Module declarations from 'libc.string' */

/* Module declarations from 'libc.stdio' */

/* Module declarations from 'cpython.object' */

/* Module declarations from 'cpython.ref' */

/* Module declarations from 'cpython.exc' */

/* Module declarations from 'cpython.module' */

/* Module declarations from 'cpython.mem' */

/* Module declarations from 'cpython.tuple' */

/* Module declarations from 'cpython.list' */

/* Module declarations from 'cpython.sequence' */

/* Module declarations from 'cpython.mapping' */

/* Module declarations from 'cpython.iterator' */

/* Module declarations from 'cpython.number' */

/* Module declarations from 'cpython.int' */

/* Module declarations from '__builtin__' */

/* Module declarations from 'cpython.bool' */
static PyTypeObject *__pyx_ptype_7cpython_4bool_bool = 0;

/* Module declarations from 'cpython.long' */

/* Module declarations from 'cpython.float' */

/* Module declarations from '__builtin__' */

/* Module declarations from 'cpython.complex' */
static PyTypeObject *__pyx_ptype_7cpython_7complex_complex = 0;

/* Module declarations from 'cpython.string' */

/* Module declarations from 'cpython.unicode' */

/* Module declarations from 'cpython.dict' */

/* Module declarations from 'cpython.instance' */

/* Module declarations from 'cpython.function' */

/* Module declarations from 'cpython.method' */

/* Module declarations from 'cpython.weakref' */

/* Module declarations from 'cpython.getargs' */

/* Module declarations from 'cpython.pythread' */

/* Module declarations from 'cpython.pystate' */

/* Module declarations from 'cpython.cobject' */

/* Module declarations from 'cpython.oldbuffer' */

/* Module declarations from 'cpython.set' */

/* Module declarations from 'cpython.buffer' */

/* Module declarations from 'cpython.bytes' */

/* Module declarations from 'cpython' */

/* Module declarations from 'cython.view' */

/* Module declarations from 'cython' */

/* Module declarations from 'numpy' */

/* Module declarations from 'numpy' */
static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *); /*proto*/
static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *, PyObject *); /*proto*/
static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *, PyObject *, PyObject *); /*proto*/
static CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/

/* Module declarations from 'libc.stdint' */

/* Module declarations from 'numpy.random.bit_generator' */
static PyTypeObject *__pyx_ptype_5numpy_6random_13bit_generator_BitGenerator = 0;
static PyTypeObject *__pyx_ptype_5numpy_6random_13bit_generator_SeedSequence = 0;
static PyTypeObject *__pyx_ptype_5numpy_6random_13bit_generator_SeedlessSequence = 0;

/* Module declarations from 'numpy.random' */

/* Module declarations from 'numpy.random.c_distributions' */

/* Module declarations from 'libc' */

/* Module declarations from 'numpy.random._bounded_integers' */
static CYTHON_INLINE uint64_t __pyx_f_5numpy_6random_17_bounded_integers__gen_mask(uint64_t); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_uint64)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_uint32)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_uint16)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_uint8)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_bool)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_int64)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_int32)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_int16)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_17_bounded_integers__rand_int8)(PyObject *, PyObject *, PyObject *, int, int, bitgen_t *, PyObject *); /*proto*/

/* Module declarations from 'numpy.random._common' */
static double *__pyx_vp_5numpy_6random_7_common_POISSON_LAM_MAX = 0;
#define __pyx_v_5numpy_6random_7_common_POISSON_LAM_MAX (*__pyx_vp_5numpy_6random_7_common_POISSON_LAM_MAX)
static double *__pyx_vp_5numpy_6random_7_common_LEGACY_POISSON_LAM_MAX = 0;
#define __pyx_v_5numpy_6random_7_common_LEGACY_POISSON_LAM_MAX (*__pyx_vp_5numpy_6random_7_common_LEGACY_POISSON_LAM_MAX)
static uint64_t *__pyx_vp_5numpy_6random_7_common_MAXSIZE = 0;
#define __pyx_v_5numpy_6random_7_common_MAXSIZE (*__pyx_vp_5numpy_6random_7_common_MAXSIZE)
static int (*__pyx_f_5numpy_6random_7_common_check_constraint)(double, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type); /*proto*/
static int (*__pyx_f_5numpy_6random_7_common_check_array_constraint)(PyArrayObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type); /*proto*/
static double (*__pyx_f_5numpy_6random_7_common_kahan_sum)(double *, npy_intp); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_7_common_double_fill)(void *, bitgen_t *, PyObject *, PyObject *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_7_common_float_fill)(void *, bitgen_t *, PyObject *, PyObject *, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_7_common_validate_output_shape)(PyObject *, PyArrayObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_7_common_cont)(void *, void *, PyObject *, PyObject *, int, PyObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_7_common_disc)(void *, void *, PyObject *, PyObject *, int, int, PyObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_7_common_cont_f)(void *, bitgen_t *, PyObject *, PyObject *, PyObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyObject *); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_7_common_cont_broadcast_3)(void *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyArrayObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyArrayObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type); /*proto*/
static PyObject *(*__pyx_f_5numpy_6random_7_common_discrete_broadcast_iii)(void *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyArrayObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type, PyArrayObject *, PyObject *, __pyx_t_5numpy_6random_7_common_constraint_type); /*proto*/

/* Module declarations from 'numpy.random._generator' */
static PyTypeObject *__pyx_ptype_5numpy_6random_10_generator_Generator = 0;
static PyTypeObject *__pyx_array_type = 0;
static PyTypeObject *__pyx_MemviewEnum_type = 0;
static PyTypeObject *__pyx_memoryview_type = 0;
static PyTypeObject *__pyx_memoryviewslice_type = 0;
static PyObject *generic = 0;
static PyObject *strided = 0;
static PyObject *indirect = 0;
static PyObject *contiguous = 0;
static PyObject *indirect_contiguous = 0;
static int __pyx_memoryview_thread_locks_used;
static PyThread_type_lock __pyx_memoryview_thread_locks[8];
static int64_t __pyx_f_5numpy_6random_10_generator__safe_sum_nonneg_int64(size_t, int64_t *); /*proto*/
static int __pyx_f_5numpy_6random_10_generator__check_bit_generator(PyObject *); /*proto*/
static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
static void *__pyx_align_pointer(void *, size_t); /*proto*/
static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
static PyObject *_unellipsify(PyObject *, int); /*proto*/
static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/
static int __pyx_memoryview_err(PyObject *, char *); /*proto*/
static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
static __Pyx_TypeInfo __Pyx_TypeInfo_nn_uint64_t = { "uint64_t", NULL, sizeof(uint64_t), { 0 }, 0, IS_UNSIGNED(uint64_t) ? 'U' : 'I', IS_UNSIGNED(uint64_t), 0 };
#define __Pyx_MODULE_NAME "numpy.random._generator"
extern int __pyx_module_is_main_numpy__random___generator;
int __pyx_module_is_main_numpy__random___generator = 0;

/* Implementation of 'numpy.random._generator' */
static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_ValueError;
static PyObject *__pyx_builtin_id;
static PyObject *__pyx_builtin_TypeError;
static PyObject *__pyx_builtin_OverflowError;
static PyObject *__pyx_builtin_RuntimeWarning;
static PyObject *__pyx_builtin_MemoryError;
static PyObject *__pyx_builtin_reversed;
static PyObject *__pyx_builtin_NotImplementedError;
static PyObject *__pyx_builtin_ImportError;
static PyObject *__pyx_builtin_enumerate;
static PyObject *__pyx_builtin_Ellipsis;
static PyObject *__pyx_builtin_IndexError;
static const char __pyx_k_O[] = "O";
static const char __pyx_k_T[] = "T";
static const char __pyx_k_a[] = "a";
static const char __pyx_k_b[] = "b";
static const char __pyx_k_c[] = "c";
static const char __pyx_k_n[] = "n";
static const char __pyx_k_p[] = "p";
static const char __pyx_k_x[] = "x";
static const char __pyx_k__2[] = "(";
static const char __pyx_k__3[] = ")";
static const char __pyx_k__5[] = "";
static const char __pyx_k_df[] = "df";
static const char __pyx_k_id[] = "id";
static const char __pyx_k_mu[] = "mu";
static const char __pyx_k_np[] = "np";
static const char __pyx_k_u4[] = "<u4";
static const char __pyx_k_add[] = "add";
static const char __pyx_k_all[] = "all";
static const char __pyx_k_any[] = "any";
static const char __pyx_k_cov[] = "cov";
static const char __pyx_k_dot[] = "dot";
static const char __pyx_k_eps[] = "eps";
static const char __pyx_k_lam[] = "lam";
static const char __pyx_k_loc[] = "loc";
static const char __pyx_k_low[] = "low";
static const char __pyx_k_max[] = "max";
static const char __pyx_k_new[] = "__new__";
static const char __pyx_k_obj[] = "obj";
static const char __pyx_k_out[] = "out";
static const char __pyx_k_str[] = "__str__";
static const char __pyx_k_svd[] = "svd";
static const char __pyx_k_tol[] = "tol";
static const char __pyx_k_zig[] = "zig";
static const char __pyx_k_atol[] = "atol";
static const char __pyx_k_axis[] = "axis";
static const char __pyx_k_base[] = "base";
static const char __pyx_k_bool[] = "bool_";
static const char __pyx_k_copy[] = "copy";
static const char __pyx_k_dict[] = "__dict__";
static const char __pyx_k_eigh[] = "eigh";
static const char __pyx_k_exit[] = "__exit__";
static const char __pyx_k_full[] = "full";
static const char __pyx_k_high[] = "high";
static const char __pyx_k_int8[] = "int8";
static const char __pyx_k_intp[] = "intp";
static const char __pyx_k_item[] = "item";
static const char __pyx_k_left[] = "left";
static const char __pyx_k_less[] = "less";
static const char __pyx_k_lock[] = "lock";
static const char __pyx_k_long[] = "long";
static const char __pyx_k_main[] = "__main__";
static const char __pyx_k_mean[] = "mean";
static const char __pyx_k_mode[] = "mode";
static const char __pyx_k_name[] = "__name__";
static const char __pyx_k_nbad[] = "nbad";
static const char __pyx_k_ndim[] = "ndim";
static const char __pyx_k_nonc[] = "nonc";
static const char __pyx_k_pack[] = "pack";
static const char __pyx_k_prod[] = "prod";
static const char __pyx_k_rtol[] = "rtol";
static const char __pyx_k_seed[] = "seed";
static const char __pyx_k_side[] = "side";
static const char __pyx_k_size[] = "size";
static const char __pyx_k_sort[] = "sort";
static const char __pyx_k_sqrt[] = "sqrt";
static const char __pyx_k_step[] = "step";
static const char __pyx_k_stop[] = "stop";
static const char __pyx_k_take[] = "take";
static const char __pyx_k_test[] = "__test__";
static const char __pyx_k_warn[] = "warn";
static const char __pyx_k_ASCII[] = "ASCII";
static const char __pyx_k_PCG64[] = "PCG64";
static const char __pyx_k_alpha[] = "alpha";
static const char __pyx_k_array[] = "array";
static const char __pyx_k_class[] = "__class__";
static const char __pyx_k_count[] = "count";
static const char __pyx_k_dfden[] = "dfden";
static const char __pyx_k_dfnum[] = "dfnum";
static const char __pyx_k_dtype[] = "dtype";
static const char __pyx_k_empty[] = "empty";
static const char __pyx_k_enter[] = "__enter__";
static const char __pyx_k_equal[] = "equal";
static const char __pyx_k_error[] = "error";
static const char __pyx_k_finfo[] = "finfo";
static const char __pyx_k_flags[] = "flags";
static const char __pyx_k_index[] = "index";
static const char __pyx_k_int16[] = "int16";
static const char __pyx_k_int32[] = "int32";
static const char __pyx_k_int64[] = "int64";
static const char __pyx_k_isnan[] = "isnan";
static const char __pyx_k_kappa[] = "kappa";
static const char __pyx_k_ngood[] = "ngood";
static const char __pyx_k_numpy[] = "numpy";
static const char __pyx_k_pcg64[] = "_pcg64";
static const char __pyx_k_pvals[] = "pvals";
static const char __pyx_k_raise[] = "raise";
static const char __pyx_k_range[] = "range";
static const char __pyx_k_ravel[] = "ravel";
static const char __pyx_k_right[] = "right";
static const char __pyx_k_scale[] = "scale";
static const char __pyx_k_shape[] = "shape";
static const char __pyx_k_sigma[] = "sigma";
static const char __pyx_k_start[] = "start";
static const char __pyx_k_state[] = "state";
static const char __pyx_k_uint8[] = "uint8";
static const char __pyx_k_zeros[] = "zeros";
static const char __pyx_k_arange[] = "arange";
static const char __pyx_k_astype[] = "astype";
static const char __pyx_k_colors[] = "colors";
static const char __pyx_k_compat[] = "compat";
static const char __pyx_k_cumsum[] = "cumsum";
static const char __pyx_k_double[] = "double";
static const char __pyx_k_encode[] = "encode";
static const char __pyx_k_format[] = "format";
static const char __pyx_k_ignore[] = "ignore";
static const char __pyx_k_import[] = "__import__";
static const char __pyx_k_matmul[] = "__matmul__";
static const char __pyx_k_method[] = "method";
static const char __pyx_k_name_2[] = "name";
static const char __pyx_k_pickle[] = "_pickle";
static const char __pyx_k_random[] = "random";
static const char __pyx_k_reduce[] = "reduce";
static const char __pyx_k_struct[] = "struct";
static const char __pyx_k_uint16[] = "uint16";
static const char __pyx_k_uint32[] = "uint32";
static const char __pyx_k_uint64[] = "uint64";
static const char __pyx_k_unique[] = "unique";
static const char __pyx_k_unpack[] = "unpack";
static const char __pyx_k_update[] = "update";
static const char __pyx_k_alpha_0[] = "alpha <= 0";
static const char __pyx_k_asarray[] = "asarray";
static const char __pyx_k_at_0x_X[] = " at 0x{:X}";
static const char __pyx_k_capsule[] = "capsule";
static const char __pyx_k_float32[] = "float32";
static const char __pyx_k_float64[] = "float64";
static const char __pyx_k_fortran[] = "fortran";
static const char __pyx_k_greater[] = "greater";
static const char __pyx_k_imatmul[] = "__imatmul__";
static const char __pyx_k_integer[] = "integer";
static const char __pyx_k_memview[] = "memview";
static const char __pyx_k_nsample[] = "nsample";
static const char __pyx_k_replace[] = "replace";
static const char __pyx_k_reshape[] = "reshape";
static const char __pyx_k_rmatmul[] = "__rmatmul__";
static const char __pyx_k_shuffle[] = "shuffle";
static const char __pyx_k_strides[] = "strides";
static const char __pyx_k_tobytes[] = "tobytes";
static const char __pyx_k_Ellipsis[] = "Ellipsis";
static const char __pyx_k_allclose[] = "allclose";
static const char __pyx_k_cholesky[] = "cholesky";
static const char __pyx_k_endpoint[] = "endpoint";
static const char __pyx_k_floating[] = "floating";
static const char __pyx_k_getstate[] = "__getstate__";
static const char __pyx_k_integers[] = "integers";
static const char __pyx_k_isfinite[] = "isfinite";
static const char __pyx_k_isnative[] = "isnative";
static const char __pyx_k_isscalar[] = "isscalar";
static const char __pyx_k_itemsize[] = "itemsize";
static const char __pyx_k_operator[] = "operator";
static const char __pyx_k_pickle_2[] = "pickle";
static const char __pyx_k_pyx_type[] = "__pyx_type";
static const char __pyx_k_reduce_2[] = "__reduce__";
static const char __pyx_k_reversed[] = "reversed";
static const char __pyx_k_setstate[] = "__setstate__";
static const char __pyx_k_subtract[] = "subtract";
static const char __pyx_k_swapaxes[] = "swapaxes";
static const char __pyx_k_warnings[] = "warnings";
static const char __pyx_k_Generator[] = "Generator";
static const char __pyx_k_TypeError[] = "TypeError";
static const char __pyx_k_enumerate[] = "enumerate";
static const char __pyx_k_left_mode[] = "left > mode";
static const char __pyx_k_marginals[] = "marginals";
static const char __pyx_k_pyx_state[] = "__pyx_state";
static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
static const char __pyx_k_IndexError[] = "IndexError";
static const char __pyx_k_ValueError[] = "ValueError";
static const char __pyx_k_empty_like[] = "empty_like";
static const char __pyx_k_issubdtype[] = "issubdtype";
static const char __pyx_k_left_right[] = "left == right";
static const char __pyx_k_less_equal[] = "less_equal";
static const char __pyx_k_logical_or[] = "logical_or";
static const char __pyx_k_mode_right[] = "mode > right";
static const char __pyx_k_numpy_dual[] = "numpy.dual";
static const char __pyx_k_pyx_result[] = "__pyx_result";
static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
static const char __pyx_k_ImportError[] = "ImportError";
static const char __pyx_k_MemoryError[] = "MemoryError";
static const char __pyx_k_PickleError[] = "PickleError";
static const char __pyx_k_check_valid[] = "check_valid";
static const char __pyx_k_default_rng[] = "default_rng";
static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
static const char __pyx_k_return_index[] = "return_index";
static const char __pyx_k_searchsorted[] = "searchsorted";
static const char __pyx_k_stringsource[] = "stringsource";
static const char __pyx_k_OverflowError[] = "OverflowError";
static const char __pyx_k_bit_generator[] = "bit_generator";
static const char __pyx_k_count_nonzero[] = "count_nonzero";
static const char __pyx_k_generator_pyx[] = "_generator.pyx";
static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
static const char __pyx_k_RuntimeWarning[] = "RuntimeWarning";
static const char __pyx_k_generator_ctor[] = "__generator_ctor";
static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
static const char __pyx_k_poisson_lam_max[] = "_poisson_lam_max";
static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
static const char __pyx_k_standard_normal[] = "standard_normal";
static const char __pyx_k_sum_pvals_1_1_0[] = "sum(pvals[:-1]) > 1.0";
static const char __pyx_k_may_share_memory[] = "may_share_memory";
static const char __pyx_k_ascontiguousarray[] = "ascontiguousarray";
static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
static const char __pyx_k_ngood_nbad_nsample[] = "ngood + nbad < nsample";
static const char __pyx_k_nsample_sum_colors[] = "nsample > sum(colors)";
static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
static const char __pyx_k_NotImplementedError[] = "NotImplementedError";
static const char __pyx_k_normalize_axis_index[] = "normalize_axis_index";
static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
static const char __pyx_k_Generator_f_line_1210[] = "Generator.f (line 1210)";
static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
static const char __pyx_k_numpy_core_multiarray[] = "numpy.core.multiarray";
static const char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
static const char __pyx_k_numpy_random__generator[] = "numpy.random._generator";
static const char __pyx_k_p_must_be_1_dimensional[] = "p must be 1-dimensional";
static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'";
static const char __pyx_k_Generator_bytes_line_489[] = "Generator.bytes (line 489)";
static const char __pyx_k_Generator_wald_line_2529[] = "Generator.wald (line 2529)";
static const char __pyx_k_Generator_zipf_line_2989[] = "Generator.zipf (line 2989)";
static const char __pyx_k_Generator_choice_line_518[] = "Generator.choice (line 518)";
static const char __pyx_k_Generator_gamma_line_1132[] = "Generator.gamma (line 1132)";
static const char __pyx_k_Generator_normal_line_939[] = "Generator.normal (line 939)";
static const char __pyx_k_Generator_power_line_1963[] = "Generator.power (line 1963)";
static const char __pyx_k_Generator_random_line_155[] = "Generator.random (line 155)";
static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
static const char __pyx_k_nsample_must_not_exceed_d[] = "nsample must not exceed %d";
static const char __pyx_k_probabilities_contain_NaN[] = "probabilities contain NaN";
static const char __pyx_k_Generator_gumbel_line_2149[] = "Generator.gumbel (line 2149)";
static const char __pyx_k_Generator_pareto_line_1766[] = "Generator.pareto (line 1766)";
static const char __pyx_k_Generator_uniform_line_762[] = "Generator.uniform (line 762)";
static const char __pyx_k_Range_exceeds_valid_bounds[] = "Range exceeds valid bounds";
static const char __pyx_k_mean_must_be_1_dimensional[] = "mean must be 1 dimensional";
static const char __pyx_k_nsample_must_be_an_integer[] = "nsample must be an integer";
static const char __pyx_k_Generator_integers_line_362[] = "Generator.integers (line 362)";
static const char __pyx_k_Generator_laplace_line_2064[] = "Generator.laplace (line 2064)";
static const char __pyx_k_Generator_poisson_line_2917[] = "Generator.poisson (line 2917)";
static const char __pyx_k_Generator_shuffle_line_4114[] = "Generator.shuffle (line 4114)";
static const char __pyx_k_Generator_weibull_line_1864[] = "Generator.weibull (line 1864)";
static const char __pyx_k_a_and_p_must_have_same_size[] = "a and p must have same size";
static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
static const char __pyx_k_nsample_must_be_nonnegative[] = "nsample must be nonnegative.";
static const char __pyx_k_Generator_binomial_line_2697[] = "Generator.binomial (line 2697)";
static const char __pyx_k_Generator_logistic_line_2268[] = "Generator.logistic (line 2268)";
static const char __pyx_k_Generator_rayleigh_line_2460[] = "Generator.rayleigh (line 2460)";
static const char __pyx_k_Generator_vonmises_line_1683[] = "Generator.vonmises (line 1683)";
static const char __pyx_k_Generator_chisquare_line_1376[] = "Generator.chisquare (line 1376)";
static const char __pyx_k_Generator_dirichlet_line_3915[] = "Generator.dirichlet (line 3915)";
static const char __pyx_k_Generator_geometric_line_3069[] = "Generator.geometric (line 3069)";
static const char __pyx_k_Generator_lognormal_line_2348[] = "Generator.lognormal (line 2348)";
static const char __pyx_k_Generator_logseries_line_3263[] = "Generator.logseries (line 3263)";
static const char __pyx_k_a_must_an_array_or_an_integer[] = "a must an array or an integer";
static const char __pyx_k_probabilities_do_not_sum_to_1[] = "probabilities do not sum to 1";
static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
static const char __pyx_k_Generator_standard_t_line_1589[] = "Generator.standard_t (line 1589)";
static const char __pyx_k_Generator_triangular_line_2597[] = "Generator.triangular (line 2597)";
static const char __pyx_k_Unsupported_dtype_r_for_random[] = "Unsupported dtype %r for random";
static const char __pyx_k_dirichlet_alpha_size_None_Draw[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. The Dirichlet distribution\n        is a conjugate prior of a multinomial distribution in Bayesian\n        inference.\n\n        Parameters\n        ----------\n        alpha : sequence of floats, length k\n            Parameter of the distribution (length ``k`` for sample of\n            length ``k``).\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            vector of length ``k`` is returned.\n\n        Returns\n        -------\n        samples : ndarray,\n            The drawn samples, of shape ``(size, k)``.\n\n        Raises\n        -------\n        ValueError\n            If any value in ``alpha`` is less than or equal to zero\n\n        Notes\n        -----\n        The Dirichlet distribution is a distribution over vectors\n        :math:`x` that fulfil the conditions :math:`x_i>0` and\n        :math:`\\sum_{i=1}^k x_i = 1`.\n\n        The probability density function :math:`p` of a\n        Dirichlet-distributed random vector :math:`X` is\n        proportional to\n\n        .. math:: p(x) \\propto \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i},\n\n        where :math:`\\alpha` is a vector containing the positive\n        concentration parameters.\n\n        The method uses the following property for computation: let :math:`Y`\n        be a random vector which has components that follow a standard gamma\n        distribution, then :math:`X = \\frac{1}{\\sum_{i=1}^k{Y_i}} Y`\n        is Dirichlet-distributed\n\n        References\n        ----------\n        .. [1] David McKay, \"Information Theory, Inference and Learning""\n               Algorithms,\" chapter 23,\n               http://www.inference.org.uk/mackay/itila/\n        .. [2] Wikipedia, \"Dirichlet distribution\",\n               https://en.wikipedia.org/wiki/Dirichlet_distribution\n\n        Examples\n        --------\n        Taking an example cited in Wikipedia, this distribution can be used if\n        one wanted to cut strings (each of initial length 1.0) into K pieces\n        with different lengths, where each piece had, on average, a designated\n        average length, but allowing some variation in the relative sizes of\n        the pieces.\n\n        >>> s = np.random.default_rng().dirichlet((10, 5, 3), 20).transpose()\n\n        >>> import matplotlib.pyplot as plt\n        >>> plt.barh(range(20), s[0])\n        >>> plt.barh(range(20), s[1], left=s[0], color='g')\n        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n        >>> plt.title(\"Lengths of Strings\")\n\n        ";
static const char __pyx_k_laplace_loc_0_0_scale_1_0_size[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float or array_like of floats, optional\n            The position, :math:`\\mu`, of the distribution peak. Default is 0.\n        scale : float or array_like of floats, optional\n            :math:`\\lambda`, the exponential decay. Default is 1. Must be non-\n            negative.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Laplace distribution.\n\n        Notes\n        -----\n        It has the probability density function\n\n        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n        The first law of Laplace, from 1774, states that the frequency\n        of an error can be expressed as an exponential function of the\n        absolute magnitude of the error, which leads to the Laplace\n        distribution. For many problems in economics and health\n        sciences, this distribution seems to model the data better\n        than the standard Gaussian distribution.\n\n        References\n        ----------\n        .. [1] Abramowitz"", M. and Stegun, I. A. (Eds.). \"Handbook of\n               Mathematical Functions with Formulas, Graphs, and Mathematical\n               Tables, 9th printing,\" New York: Dover, 1972.\n        .. [2] Kotz, Samuel, et. al. \"The Laplace Distribution and\n               Generalizations, \" Birkhauser, 2001.\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n        .. [4] Wikipedia, \"Laplace distribution\",\n               https://en.wikipedia.org/wiki/Laplace_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution\n\n        >>> loc, scale = 0., 1.\n        >>> s = np.random.default_rng().laplace(loc, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, density=True)\n        >>> x = np.arange(-8., 8., .01)\n        >>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)\n        >>> plt.plot(x, pdf)\n\n        Plot Gaussian for comparison:\n\n        >>> g = (1/(scale * np.sqrt(2 * np.pi)) *\n        ...      np.exp(-(x - loc)**2 / (2 * scale**2)))\n        >>> plt.plot(x,g)\n\n        ";
static const char __pyx_k_poisson_lam_1_0_size_None_Draw[] = "\n        poisson(lam=1.0, size=None)\n\n        Draw samples from a Poisson distribution.\n\n        The Poisson distribution is the limit of the binomial distribution\n        for large N.\n\n        Parameters\n        ----------\n        lam : float or array_like of floats\n            Expectation of interval, must be >= 0. A sequence of expectation\n            intervals must be broadcastable over the requested size.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``lam`` is a scalar. Otherwise,\n            ``np.array(lam).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Poisson distribution.\n\n        Notes\n        -----\n        The Poisson distribution\n\n        .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n        For events with an expected separation :math:`\\lambda` the Poisson\n        distribution :math:`f(k; \\lambda)` describes the probability of\n        :math:`k` events occurring within the observed\n        interval :math:`\\lambda`.\n\n        Because the output is limited to the range of the C int64 type, a\n        ValueError is raised when `lam` is within 10 sigma of the maximum\n        representable value.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Poisson Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/PoissonDistribution.html\n        .. [2] Wikipedia, \"Poisson distribution\",\n               https://en.wikipedia.org/wiki/Poisson_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> import numpy as np\n        >>> rng = np.random.default_rng()\n        >>> s = rng.poiss""on(5, 10000)\n\n        Display histogram of the sample:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 14, density=True)\n        >>> plt.show()\n\n        Draw each 100 values for lambda 100 and 500:\n\n        >>> s = rng.poisson(lam=(100., 500.), size=(100, 2))\n\n        ";
static const char __pyx_k_standard_cauchy_size_None_Draw[] = "\n        standard_cauchy(size=None)\n\n        Draw samples from a standard Cauchy distribution with mode = 0.\n\n        Also known as the Lorentz distribution.\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        Notes\n        -----\n        The probability density function for the full Cauchy distribution is\n\n        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n        :math:`\\gamma=1`\n\n        The Cauchy distribution arises in the solution to the driven harmonic\n        oscillator problem, and also describes spectral line broadening. It\n        also describes the distribution of values at which a line tilted at\n        a random angle will cut the x axis.\n\n        When studying hypothesis tests that assume normality, seeing how the\n        tests perform on data from a Cauchy distribution is a good indicator of\n        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n        very much like a Gaussian distribution, but with heavier tails.\n\n        References\n        ----------\n        .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n              Distribution\",\n              https://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n        .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n              Wolfram Web Resource.\n              http://mathworld.wolfram.com/CauchyDistribution.html\n        .. [3] Wikipedia, \"Cauchy distribution\"\n              https://en.wikipedia.org/wiki""/Cauchy_distribution\n\n        Examples\n        --------\n        Draw samples and plot the distribution:\n\n        >>> import matplotlib.pyplot as plt\n        >>> s = np.random.default_rng().standard_cauchy(1000000)\n        >>> s = s[(s>-25) & (s<25)]  # truncate distribution so it plots well\n        >>> plt.hist(s, bins=100)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_standard_exponential_size_None[] = "\n        standard_exponential(size=None, dtype=np.float64, method='zig', out=None)\n\n        Draw samples from the standard exponential distribution.\n\n        `standard_exponential` is identical to the exponential distribution\n        with a scale parameter of 1.\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\n        dtype : dtype, optional\n            Desired dtype of the result, only `float64` and `float32` are supported.\n            Byteorder must be native. The default value is np.float64.\n        method : str, optional\n            Either 'inv' or 'zig'. 'inv' uses the default inverse CDF method.\n            'zig' uses the much faster Ziggurat method of Marsaglia and Tsang.\n        out : ndarray, optional\n            Alternative output array in which to place the result. If size is not None,\n            it must have the same shape as the provided size and must match the type of\n            the output values.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        Output a 3x8000 array:\n\n        >>> n = np.random.default_rng().standard_exponential((3, 8000))\n\n        ";
static const char __pyx_k_standard_gamma_shape_size_None[] = "\n        standard_gamma(shape, size=None, dtype=np.float64, out=None)\n\n        Draw samples from a standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float or array_like of floats\n            Parameter, must be non-negative.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``shape`` is a scalar.  Otherwise,\n            ``np.array(shape).size`` samples are drawn.\n        dtype : dtype, optional\n            Desired dtype of the result, only `float64` and `float32` are supported.\n            Byteorder must be native. The default value is np.float64.\n        out : ndarray, optional\n            Alternative output array in which to place the result. If size is\n            not None, it must have the same shape as the provided size and\n            must match the type of the output values.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized standard gamma distribution.\n\n        See Also\n        --------\n        scipy.stats.gamma : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events"" are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma distribution\",\n               https://en.wikipedia.org/wiki/Gamma_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 1. # mean and width\n        >>> s = np.random.default_rng().standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps  # doctest: +SKIP\n        >>> count, bins, ignored = plt.hist(s, 50, density=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/  # doctest: +SKIP\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')  # doctest: +SKIP\n        >>> plt.show()\n\n        ";
static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
static const char __pyx_k_wald_mean_scale_size_None_Draw[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian. Some references claim that the Wald is an inverse Gaussian\n        with mean equal to 1, but this is by no means universal.\n\n        The inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name inverse Gaussian\n        because there is an inverse relationship between the time to cover a\n        unit distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : float or array_like of floats\n            Distribution mean, must be > 0.\n        scale : float or array_like of floats\n            Scale parameter, must be > 0.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``mean`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(mean, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Wald distribution.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the inverse Gaussian distribution first arise\n        from attempts to model Brownian motion. It is also a\n        competitor to the Weibull for use in reliability modeling and\n        modeling stock returns and interest rate processes.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., Wald Distribution,\n               http""s://web.archive.org/web/20090423014010/http://www.brighton-webs.co.uk:80/distributions/wald.asp\n        .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n               Distribution: Theory : Methodology, and Applications\", CRC Press,\n               1988.\n        .. [3] Wikipedia, \"Inverse Gaussian distribution\"\n               https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.default_rng().wald(3, 2, 100000), bins=200, density=True)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_Axis_argument_is_only_supported[] = "Axis argument is only supported on ndarray objects";
static const char __pyx_k_Generator_multinomial_line_3548[] = "Generator.multinomial (line 3548)";
static const char __pyx_k_Generator_permutation_line_4256[] = "Generator.permutation (line 4256)";
static const char __pyx_k_When_method_is_count_sum_colors[] = "When method is 'count', sum(colors) must not exceed %d";
static const char __pyx_k_binomial_n_p_size_None_Draw_sam[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer >= 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : int or array_like of ints\n            Parameter of the distribution, >= 0. Floats are also accepted,\n            but they will be truncated to integers.\n        p : float or array_like of floats\n            Parameter of the distribution, >= 0 and <=1.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``n`` and ``p`` are both scalars.\n            Otherwise, ``np.broadcast(n, p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized binomial distribution, where\n            each sample is equal to the number of successes over the n trials.\n\n        See Also\n        --------\n        scipy.stats.binom : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the binomial distribution is\n\n        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n        where :math:`n` is the number of trials, :math:`p` is the probability\n        of success, and :math:`N` is the number of successes.\n\n        When estimating the standard error of a proportion in a population by\n        using a random sample, the normal distribution works well unless the\n        product p*n <=5, where p = population proportion estimate, and n =\n        number of samples, in which case the binom""ial distribution is used\n        instead. For example, a sample of 15 people shows 4 who are left\n        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n        so the binomial distribution should be used in this case.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n               Springer-Verlag, 2002.\n        .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/BinomialDistribution.html\n        .. [5] Wikipedia, \"Binomial distribution\",\n               https://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> rng = np.random.default_rng()\n        >>> n, p = 10, .5  # number of trials, probability of each trial\n        >>> s = rng.binomial(n, p, 1000)\n        # result of flipping a coin 10 times, tested 1000 times.\n\n        A real world example. A company drills 9 wild-cat oil exploration\n        wells, each with an estimated probability of success of 0.1. All nine\n        wells fail. What is the probability of that happening?\n\n        Let's do 20,000 trials of the model, and count the number that\n        generate zero positive results.\n\n        >>> sum(rng.binomial(9, 0.1, 20000) == 0)/20000.\n        # answer = 0.38885, or 38%.\n\n        ";
static const char __pyx_k_bytes_length_Return_random_byte[] = "\n        bytes(length)\n\n        Return random bytes.\n\n        Parameters\n        ----------\n        length : int\n            Number of random bytes.\n\n        Returns\n        -------\n        out : str\n            String of length `length`.\n\n        Examples\n        --------\n        >>> np.random.default_rng().bytes(10)\n        ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n        ";
static const char __pyx_k_chisquare_df_size_None_Draw_sam[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard normal\n        distributions (mean 0, variance 1), are squared and summed, the\n        resulting distribution is chi-square (see Notes).  This distribution\n        is often used in hypothesis testing.\n\n        Parameters\n        ----------\n        df : float or array_like of floats\n             Number of degrees of freedom, must be > 0.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``df`` is a scalar.  Otherwise,\n            ``np.array(df).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized chi-square distribution.\n\n        Raises\n        ------\n        ValueError\n            When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n            is given.\n\n        Notes\n        -----\n        The variable obtained by summing the squares of `df` independent,\n        standard normally distributed random variables:\n\n        .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n        is chi-square distributed, denoted\n\n        .. math:: Q \\sim \\chi^2_k.\n\n        The probability density function of the chi-squared distribution is\n\n        .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n                         x^{k/2 - 1} e^{-x/2},\n\n        where :math:`\\Gamma` is the gamma function,\n\n        .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n        References\n        ----------\n        .. [1] NIST \"Engineering Statistics Handbook\"\n               https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n\n        Examples\n        --------\n        >>> np"".random.default_rng().chisquare(2,4)\n        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272]) # random\n\n        ";
static const char __pyx_k_choice_a_size_None_replace_True[] = "\n        choice(a, size=None, replace=True, p=None, axis=0, shuffle=True)\n\n        Generates a random sample from a given 1-D array\n\n        Parameters\n        ----------\n        a : {array_like, int}\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated from np.arange(a).\n        size : {int, tuple[int]}, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn from the 1-d `a`. If `a` has more\n            than one dimension, the `size` shape will be inserted into the\n            `axis` dimension, so the output ``ndim`` will be ``a.ndim - 1 +\n            len(size)``. Default is None, in which case a single value is\n            returned.\n        replace : bool, optional\n            Whether the sample is with or without replacement\n        p : 1-D array_like, optional\n            The probabilities associated with each entry in a.\n            If not given the sample assumes a uniform distribution over all\n            entries in a.\n        axis : int, optional\n            The axis along which the selection is performed. The default, 0,\n            selects by row.\n        shuffle : bool, optional\n            Whether the sample is shuffled when sampling without replacement.\n            Default is True, False provides a speedup.\n\n        Returns\n        -------\n        samples : single item or ndarray\n            The generated random samples\n\n        Raises\n        ------\n        ValueError\n            If a is an int and less than zero, if p is not 1-dimensional, if\n            a is array-like with a size 0, if p is not a vector of\n            probabilities, if a and p have different lengths, or if\n            replace=False and the sample size is greater than the population\n            size.\n\n        See Also\n        --------\n        integers, shuffle, permutation\n\n        Example""s\n        --------\n        Generate a uniform random sample from np.arange(5) of size 3:\n\n        >>> rng = np.random.default_rng()\n        >>> rng.choice(5, 3)\n        array([0, 3, 4]) # random\n        >>> #This is equivalent to rng.integers(0,5,3)\n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n\n        >>> rng.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0]) # random\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n        replacement:\n\n        >>> rng.choice(5, 3, replace=False)\n        array([3,1,0]) # random\n        >>> #This is equivalent to rng.permutation(np.arange(5))[:3]\n\n        Generate a non-uniform random sample from np.arange(5) of size\n        3 without replacement:\n\n        >>> rng.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0]) # random\n\n        Any of the above can be repeated with an arbitrary array-like\n        instead of just integers. For instance:\n\n        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n        >>> rng.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random\n              dtype='<U11')\n\n        ";
static const char __pyx_k_f_dfnum_dfden_size_None_Draw_sa[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from an F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n        freedom in denominator), where both parameters must be greater than\n        zero.\n\n        The random variate of the F distribution (also known as the\n        Fisher distribution) is a continuous probability distribution\n        that arises in ANOVA tests, and is the ratio of two chi-square\n        variates.\n\n        Parameters\n        ----------\n        dfnum : float or array_like of floats\n            Degrees of freedom in numerator, must be > 0.\n        dfden : float or array_like of float\n            Degrees of freedom in denominator, must be > 0.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``dfnum`` and ``dfden`` are both scalars.\n            Otherwise, ``np.broadcast(dfnum, dfden).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.f : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The F statistic is used to compare in-group variances to between-group\n        variances. Calculating the distribution depends on the sampling, and\n        so it is a function of the respective degrees of freedom in the\n        problem.  The variable `dfnum` is the number of samples minus one, the\n        between-groups degrees of freedom, while `dfden` is the within-groups\n        degrees of freedom, the sum of the number of samples in each group\n        minus the nu""mber of groups.\n\n        References\n        ----------\n        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [2] Wikipedia, \"F-distribution\",\n               https://en.wikipedia.org/wiki/F-distribution\n\n        Examples\n        --------\n        An example from Glantz[1], pp 47-40:\n\n        Two groups, children of diabetics (25 people) and children from people\n        without diabetes (25 controls). Fasting blood glucose was measured,\n        case group had a mean value of 86.1, controls had a mean value of\n        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n        data consistent with the null hypothesis that the parents diabetic\n        status does not affect their children's blood glucose levels?\n        Calculating the F statistic from the data gives a value of 36.01.\n\n        Draw samples from the distribution:\n\n        >>> dfnum = 1. # between group degrees of freedom\n        >>> dfden = 48. # within groups degrees of freedom\n        >>> s = np.random.default_rng().f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> np.sort(s)[-10]\n        7.61988120985 # random\n\n        So there is about a 1% chance that the F statistic will exceed 7.62,\n        the measured value is 36, so the null hypothesis is rejected at the 1%\n        level.\n\n        ";
static const char __pyx_k_gamma_shape_scale_1_0_size_None[] = "\n        gamma(shape, scale=1.0, size=None)\n\n        Draw samples from a Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n        \"theta\"), where both parameters are > 0.\n\n        Parameters\n        ----------\n        shape : float or array_like of floats\n            The shape of the gamma distribution. Must be non-negative.\n        scale : float or array_like of floats, optional\n            The scale of the gamma distribution. Must be non-negative.\n            Default is equal to 1.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``shape`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized gamma distribution.\n\n        See Also\n        --------\n        scipy.stats.gamma : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n          ""     http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma distribution\",\n               https://en.wikipedia.org/wiki/Gamma_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)\n        >>> s = np.random.default_rng().gamma(shape, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps  # doctest: +SKIP\n        >>> count, bins, ignored = plt.hist(s, 50, density=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /  # doctest: +SKIP\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')  # doctest: +SKIP\n        >>> plt.show()\n\n        ";
static const char __pyx_k_geometric_p_size_None_Draw_samp[] = "\n        geometric(p, size=None)\n\n        Draw samples from the geometric distribution.\n\n        Bernoulli trials are experiments with one of two outcomes:\n        success or failure (an example of such an experiment is flipping\n        a coin).  The geometric distribution models the number of trials\n        that must be run in order to achieve success.  It is therefore\n        supported on the positive integers, ``k = 1, 2, ...``.\n\n        The probability mass function of the geometric distribution is\n\n        .. math:: f(k) = (1 - p)^{k - 1} p\n\n        where `p` is the probability of success of an individual trial.\n\n        Parameters\n        ----------\n        p : float or array_like of floats\n            The probability of success of an individual trial.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``p`` is a scalar.  Otherwise,\n            ``np.array(p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized geometric distribution.\n\n        Examples\n        --------\n        Draw ten thousand values from the geometric distribution,\n        with the probability of an individual success equal to 0.35:\n\n        >>> z = np.random.default_rng().geometric(p=0.35, size=10000)\n\n        How many trials succeeded after a single run?\n\n        >>> (z == 1).sum() / 10000.\n        0.34889999999999999 #random\n\n        ";
static const char __pyx_k_gumbel_loc_0_0_scale_1_0_size_N[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and\n        scale.  For more information on the Gumbel distribution, see\n        Notes and References below.\n\n        Parameters\n        ----------\n        loc : float or array_like of floats, optional\n            The location of the mode of the distribution. Default is 0.\n        scale : float or array_like of floats, optional\n            The scale parameter of the distribution. Default is 1. Must be non-\n            negative.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Gumbel distribution.\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme\n        Value Type I) distribution is one of a class of Generalized Extreme\n        Value (GEV) distributions used in modeling extreme value problems.\n        The Gumbel is a special case of the Extreme Value Type I distribution\n        for maximums from distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and\n        :math:`\\beta` is the scale parameter.\n\n        The Gum""bel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and\n        rainfall rates.  It is a \"fat-tailed\" distribution - the probability of\n        an event in the tail of the distribution is larger than if one used a\n        Gaussian, hence the surprisingly frequent occurrence of 100-year\n        floods. Floods were initially modeled as a Gaussian process, which\n        underestimated the frequency of extreme events.\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance\n        of :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        .. [1] Gumbel, E. J., \"Statistics of Extremes,\"\n               New York: Columbia University Press, 1958.\n        .. [2] Reiss, R.-D. and Thomas, M., \"Statistical Analysis of Extreme\n               Values from Insurance, Finance, Hydrology and Other Fields,\"\n               Basel: Birkhauser Verlag, 2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> rng = np.random.default_rng()\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = rng.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, density=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compa""re to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = rng.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, density=True)\n        >>> beta = np.std(maxima) * np.sqrt(6) / np.pi\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
static const char __pyx_k_hypergeometric_ngood_nbad_nsamp[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a hypergeometric distribution with specified\n        parameters, `ngood` (ways to make a good selection), `nbad` (ways to make\n        a bad selection), and `nsample` (number of items sampled, which is less\n        than or equal to the sum ``ngood + nbad``).\n\n        Parameters\n        ----------\n        ngood : int or array_like of ints\n            Number of ways to make a good selection.  Must be nonnegative and\n            less than 10**9.\n        nbad : int or array_like of ints\n            Number of ways to make a bad selection.  Must be nonnegative and\n            less than 10**9.\n        nsample : int or array_like of ints\n            Number of items sampled.  Must be nonnegative and less than\n            ``ngood + nbad``.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if `ngood`, `nbad`, and `nsample`\n            are all scalars.  Otherwise, ``np.broadcast(ngood, nbad, nsample).size``\n            samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized hypergeometric distribution. Each\n            sample is the number of good items within a randomly selected subset of\n            size `nsample` taken from a set of `ngood` good items and `nbad` bad items.\n\n        See Also\n        --------\n        multivariate_hypergeometric : Draw samples from the multivariate\n            hypergeometric distribution.\n        scipy.stats.hypergeom : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric dist""ribution is\n\n        .. math:: P(x) = \\frac{\\binom{g}{x}\\binom{b}{n-x}}{\\binom{g+b}{n}},\n\n        where :math:`0 \\le x \\le n` and :math:`n-b \\le x \\le g`\n\n        for P(x) the probability of ``x`` good results in the drawn sample,\n        g = `ngood`, b = `nbad`, and n = `nsample`.\n\n        Consider an urn with black and white marbles in it, `ngood` of them\n        are black and `nbad` are white. If you draw `nsample` balls without\n        replacement, then the hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the binomial\n        distribution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn with\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the binomial.\n\n        The arguments `ngood` and `nbad` each must be less than `10**9`. For\n        extremely large arguments, the algorithm that is used to compute the\n        samples [4]_ breaks down because of loss of precision in floating point\n        calculations.  For such large values, if `nsample` is not also large,\n        the distribution can be approximated with the binomial distribution,\n        `binomial(n=nsample, p=ngood/(ngood + nbad))`.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric distribution\",\n               https://en.wikipedia.org/wiki/Hypergeometric_distribution\n        .. [4] Stadlober, Ernst, \"The ratio of uniforms approach for generating\n               discrete random"" variates\", Journal of Computational and Applied\n               Mathematics, 31, pp. 181-189 (1990).\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> rng = np.random.default_rng()\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = rng.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> from matplotlib.pyplot import hist\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = rng.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
static const char __pyx_k_integers_low_high_None_size_Non[] = "\n        integers(low, high=None, size=None, dtype=np.int64, endpoint=False)\n\n        Return random integers from `low` (inclusive) to `high` (exclusive), or\n        if endpoint=True, `low` (inclusive) to `high` (inclusive). Replaces\n        `RandomState.randint` (with endpoint=False) and\n        `RandomState.random_integers` (with endpoint=True)\n\n        Return random integers from the \"discrete uniform\" distribution of\n        the specified dtype. If `high` is None (the default), then results are\n        from 0 to `low`.\n\n        Parameters\n        ----------\n        low : int or array-like of ints\n            Lowest (signed) integers to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is 0 and this value is\n            used for `high`).\n        high : int or array-like of ints, optional\n            If provided, one above the largest (signed) integer to be drawn\n            from the distribution (see above for behavior if ``high=None``).\n            If array-like, must contain integer values\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\n        dtype : dtype, optional\n            Desired dtype of the result. Byteorder must be native.\n            The default value is np.int64.\n        endpoint : bool, optional\n            If true, sample from the interval [low, high] instead of the\n            default [low, high)\n            Defaults to False\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        Notes\n        -----\n        When using broadcasting with uint64 dtypes, the maximum value (2**64)\n        cannot be repre""sented as a standard integer type. The high array (or\n        low if high is None) must have object dtype, e.g., array([2**64]).\n\n        Examples\n        --------\n        >>> rng = np.random.default_rng()\n        >>> rng.integers(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])  # random\n        >>> rng.integers(1, size=10)\n        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n        >>> rng.integers(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])  # random\n\n        Generate a 1 x 3 array with 3 different upper bounds\n\n        >>> rng.integers(1, [3, 5, 10])\n        array([2, 2, 9])  # random\n\n        Generate a 1 by 3 array with 3 different lower bounds\n\n        >>> rng.integers([1, 5, 7], 10)\n        array([9, 8, 7])  # random\n\n        Generate a 2 by 4 array using broadcasting with dtype of uint8\n\n        >>> rng.integers([1, 3, 5, 7], [[10], [20]], dtype=np.uint8)\n        array([[ 8,  6,  9,  7],\n               [ 1, 16,  9, 12]], dtype=uint8)  # random\n\n        References\n        ----------\n        .. [1] Daniel Lemire., \"Fast Random Integer Generation in an Interval\",\n               ACM Transactions on Modeling and Computer Simulation 29 (1), 2019,\n               http://arxiv.org/abs/1805.10941.\n\n        ";
static const char __pyx_k_logistic_loc_0_0_scale_1_0_size[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a logistic distribution.\n\n        Samples are drawn from a logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float or array_like of floats, optional\n            Parameter of the distribution. Default is 0.\n        scale : float or array_like of floats, optional\n            Parameter of the distribution. Must be non-negative.\n            Default is 1.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized logistic distribution.\n\n        See Also\n        --------\n        scipy.stats.logistic : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Logistic distribution is\n\n        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n        where :math:`\\mu` = location and :math:`s` = scale.\n\n        The Logistic distribution is used in Extreme Value problems where it\n        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n        the World Chess Federation (FIDE) where it is used in the Elo ranking\n        system, assuming the performance of each player is a logistically\n        distributed random variable.\n\n        References\n        ----------\n        .. [1] Reiss, R.-D. and Thomas M. (2001), \"Statistical Analysis of\n               Extreme Values, from Insurance, Finance, Hydr""ology and Other\n               Fields,\" Birkhauser Verlag, Basel, pp 132-133.\n        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LogisticDistribution.html\n        .. [3] Wikipedia, \"Logistic-distribution\",\n               https://en.wikipedia.org/wiki/Logistic_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> loc, scale = 10, 1\n        >>> s = np.random.default_rng().logistic(loc, scale, 10000)\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2)\n        >>> lgst_val = logist(bins, loc, scale)\n        >>> plt.plot(bins, lgst_val * count.max() / lgst_val.max())\n        >>> plt.show()\n\n        ";
static const char __pyx_k_lognormal_mean_0_0_sigma_1_0_si[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Draw samples from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean,\n        standard deviation, and array shape.  Note that the mean and standard\n        deviation are not the values for the distribution itself, but of the\n        underlying normal distribution it is derived from.\n\n        Parameters\n        ----------\n        mean : float or array_like of floats, optional\n            Mean value of the underlying normal distribution. Default is 0.\n        sigma : float or array_like of floats, optional\n            Standard deviation of the underlying normal distribution. Must be\n            non-negative. Default is 1.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``mean`` and ``sigma`` are both scalars.\n            Otherwise, ``np.broadcast(mean, sigma).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized log-normal distribution.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.  The probability density function for the log-normal\n        distribution is:\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n        deviation of the normally distributed logarithm of the variable.\n        A log-normal distribution results if a random variable is the *product*\n   ""     of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        References\n        ----------\n        .. [1] Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal\n               Distributions across the Sciences: Keys and Clues,\"\n               BioScience, Vol. 51, No. 5, May, 2001.\n               https://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n        .. [2] Reiss, R.D. and Thomas, M., \"Statistical Analysis of Extreme\n               Values,\" Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> rng = np.random.default_rng()\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = rng.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, density=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density\n        function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> rng = rng\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + rng.standard_normal(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale value""s to be positive\n        >>> count, bins, ignored = plt.hist(b, 100, density=True, align='mid')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, color='r', linewidth=2)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_logseries_p_size_None_Draw_samp[] = "\n        logseries(p, size=None)\n\n        Draw samples from a logarithmic series distribution.\n\n        Samples are drawn from a log series distribution with specified\n        shape parameter, 0 < ``p`` < 1.\n\n        Parameters\n        ----------\n        p : float or array_like of floats\n            Shape parameter for the distribution.  Must be in the range (0, 1).\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``p`` is a scalar.  Otherwise,\n            ``np.array(p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized logarithmic series distribution.\n\n        See Also\n        --------\n        scipy.stats.logser : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability mass function for the Log Series distribution is\n\n        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n        where p = probability.\n\n        The log series distribution is frequently used to represent species\n        richness and occurrence, first proposed by Fisher, Corbet, and\n        Williams in 1943 [2].  It may also be used to model the numbers of\n        occupants seen in cars [3].\n\n        References\n        ----------\n        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n               species diversity through the log series distribution of\n               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n               relation between the number of species and the number of\n               individuals in a r""andom sample of an animal population.\n               Journal of Animal Ecology, 12:42-58.\n        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"Logarithmic distribution\",\n               https://en.wikipedia.org/wiki/Logarithmic_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = .6\n        >>> s = np.random.default_rng().logseries(a, 10000)\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s)\n\n        #   plot against distribution\n\n        >>> def logseries(k, p):\n        ...     return -p**k/(k*np.log(1-p))\n        >>> plt.plot(bins, logseries(bins, a) * count.max()/\n        ...          logseries(bins, a).max(), 'r')\n        >>> plt.show()\n\n        ";
static const char __pyx_k_multinomial_n_pvals_size_None_D[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalization of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the\n        outcome was ``i``.\n\n        Parameters\n        ----------\n        n : int or array-like of ints\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            must sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> rng = np.random.default_rng()\n        >>> rng.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])  # random\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> rng.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])  # rando""m\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        Now, do one experiment throwing the dice 10 time, and 10 times again,\n        and another throwing the dice 20 times, and 20 times again:\n\n        >>> rng.multinomial([[10], [20]], [1/6.]*6, size=(2, 2))\n        array([[[2, 4, 0, 1, 2, 1],\n                [1, 3, 0, 3, 1, 2]],\n               [[1, 4, 4, 4, 4, 3],\n                [3, 3, 2, 5, 5, 2]]])  # random\n\n        The first array shows the outcomes of throwing the dice 10 times, and\n        the second shows the outcomes from throwing the dice 20 times.\n\n        A loaded die is more likely to land on number 6:\n\n        >>> rng.multinomial(100, [1/7.]*5 + [2/7.])\n        array([11, 16, 14, 17, 16, 26])  # random\n\n        The probability inputs should be normalized. As an implementation\n        detail, the value of the last entry is ignored and assumed to take\n        up any leftover probability mass, but this should not be relied on.\n        A biased coin which has twice as much weight on one side as on the\n        other should be sampled like so:\n\n        >>> rng.multinomial(100, [1.0 / 3, 2.0 / 3])  # RIGHT\n        array([38, 62])  # random\n\n        not like:\n\n        >>> rng.multinomial(100, [1.0, 2.0])  # WRONG\n        Traceback (most recent call last):\n        ValueError: pvals < 0, pvals > 1 or pvals contains NaNs\n\n        ";
static const char __pyx_k_multivariate_hypergeometric_col[] = "\n        multivariate_hypergeometric(colors, nsample, size=None,\n                                    method='marginals')\n\n        Generate variates from a multivariate hypergeometric distribution.\n\n        The multivariate hypergeometric distribution is a generalization\n        of the hypergeometric distribution.\n\n        Choose ``nsample`` items at random without replacement from a\n        collection with ``N`` distinct types.  ``N`` is the length of\n        ``colors``, and the values in ``colors`` are the number of occurrences\n        of that type in the collection.  The total number of items in the\n        collection is ``sum(colors)``.  Each random variate generated by this\n        function is a vector of length ``N`` holding the counts of the\n        different types that occurred in the ``nsample`` items.\n\n        The name ``colors`` comes from a common description of the\n        distribution: it is the probability distribution of the number of\n        marbles of each color selected without replacement from an urn\n        containing marbles of different colors; ``colors[i]`` is the number\n        of marbles in the urn with color ``i``.\n\n        Parameters\n        ----------\n        colors : sequence of integers\n            The number of each type of item in the collection from which\n            a sample is drawn.  The values in ``colors`` must be nonnegative.\n            To avoid loss of precision in the algorithm, ``sum(colors)``\n            must be less than ``10**9`` when `method` is \"marginals\".\n        nsample : int\n            The number of items selected.  ``nsample`` must not be greater\n            than ``sum(colors)``.\n        size : int or tuple of ints, optional\n            The number of variates to generate, either an integer or a tuple\n            holding the shape of the array of variates.  If the given size is,\n            e.g., ``(k, m)``, then ``k * m`` variates are drawn, where one\n            variate is ""a vector of length ``len(colors)``, and the return value\n            has shape ``(k, m, len(colors))``.  If `size` is an integer, the\n            output has shape ``(size, len(colors))``.  Default is None, in\n            which case a single variate is returned as an array with shape\n            ``(len(colors),)``.\n        method : string, optional\n            Specify the algorithm that is used to generate the variates.\n            Must be 'count' or 'marginals' (the default).  See the Notes\n            for a description of the methods.\n\n        Returns\n        -------\n        variates : ndarray\n            Array of variates drawn from the multivariate hypergeometric\n            distribution.\n\n        See Also\n        --------\n        hypergeometric : Draw samples from the (univariate) hypergeometric\n            distribution.\n\n        Notes\n        -----\n        The two methods do not return the same sequence of variates.\n\n        The \"count\" algorithm is roughly equivalent to the following numpy\n        code::\n\n            choices = np.repeat(np.arange(len(colors)), colors)\n            selection = np.random.choice(choices, nsample, replace=False)\n            variate = np.bincount(selection, minlength=len(colors))\n\n        The \"count\" algorithm uses a temporary array of integers with length\n        ``sum(colors)``.\n\n        The \"marginals\" algorithm generates a variate by using repeated\n        calls to the univariate hypergeometric sampler.  It is roughly\n        equivalent to::\n\n            variate = np.zeros(len(colors), dtype=np.int64)\n            # `remaining` is the cumulative sum of `colors` from the last\n            # element to the first; e.g. if `colors` is [3, 1, 5], then\n            # `remaining` is [9, 6, 5].\n            remaining = np.cumsum(colors[::-1])[::-1]\n            for i in range(len(colors)-1):\n                if nsample < 1:\n                    break\n                variate[i] = hypergeometr""ic(colors[i], remaining[i+1],\n                                           nsample)\n                nsample -= variate[i]\n            variate[-1] = nsample\n\n        The default method is \"marginals\".  For some cases (e.g. when\n        `colors` contains relatively small integers), the \"count\" method\n        can be significantly faster than the \"marginals\" method.  If\n        performance of the algorithm is important, test the two methods\n        with typical inputs to decide which works best.\n\n        .. versionadded:: 1.18.0\n\n        Examples\n        --------\n        >>> colors = [16, 8, 4]\n        >>> seed = 4861946401452\n        >>> gen = np.random.Generator(np.random.PCG64(seed))\n        >>> gen.multivariate_hypergeometric(colors, 6)\n        array([5, 0, 1])\n        >>> gen.multivariate_hypergeometric(colors, 6, size=3)\n        array([[5, 0, 1],\n               [2, 2, 2],\n               [3, 3, 0]])\n        >>> gen.multivariate_hypergeometric(colors, 6, size=(2, 2))\n        array([[[3, 2, 1],\n                [3, 2, 1]],\n               [[4, 1, 1],\n                [3, 2, 1]]])\n        ";
static const char __pyx_k_multivariate_normal_mean_cov_si[] = "\n        multivariate_normal(mean, cov, size=None, check_valid='warn', tol=1e-8)\n\n        Draw random samples from a multivariate normal distribution.\n\n        The multivariate normal, multinormal or Gaussian distribution is a\n        generalization of the one-dimensional normal distribution to higher\n        dimensions.  Such a distribution is specified by its mean and\n        covariance matrix.  These parameters are analogous to the mean\n        (average or \"center\") and variance (standard deviation, or \"width,\"\n        squared) of the one-dimensional normal distribution.\n\n        Parameters\n        ----------\n        mean : 1-D array_like, of length N\n            Mean of the N-dimensional distribution.\n        cov : 2-D array_like, of shape (N, N)\n            Covariance matrix of the distribution. It must be symmetric and\n            positive-semidefinite for proper sampling.\n        size : int or tuple of ints, optional\n            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n            If no shape is specified, a single (`N`-D) sample is returned.\n        check_valid : { 'warn', 'raise', 'ignore' }, optional\n            Behavior when the covariance matrix is not positive semidefinite.\n        tol : float, optional\n            Tolerance when checking the singular values in covariance matrix.\n            cov is cast to double before the check.\n        method : { 'svd', 'eigh', 'cholesky'}, optional\n            The cov input is used to compute a factor matrix A such that\n            ``A @ A.T = cov``. This argument is used to select the method\n            used to compute the factor matrix A. The default method 'svd' is\n            the slowest, while 'cholesky' is the fastest but less robust than\n            the slowest method. The method `eigh` uses"" eigen decomposition to\n            compute A and is faster than svd but slower than cholesky.\n\n            .. versionadded:: 1.18.0\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (`cov` is a multiple of the identity matrix)\n          - Diagonal covariance (`cov` has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0, 0]\n        >>> cov = [[1, 0], [0, 100]]  # diagonal covariance\n\n        Diagonal covariance means that points are oriented along x or y-axis:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x, y = np.random.default_rng().multivariate_normal(mean, cov, 5000).T\n        >>> plt.plot(x, y, 'x')\n        >>> plt.axis('equal')\n        >>> plt.show()\n\n        Note that the covariance matrix must be positive ""semidefinite (a.k.a.\n        nonnegative-definite). Otherwise, the behavior of this method is\n        undefined and backwards compatibility is not guaranteed.\n\n        References\n        ----------\n        .. [1] Papoulis, A., \"Probability, Random Variables, and Stochastic\n               Processes,\" 3rd ed., New York: McGraw-Hill, 1991.\n        .. [2] Duda, R. O., Hart, P. E., and Stork, D. G., \"Pattern\n               Classification,\" 2nd ed., New York: Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1, 2)\n        >>> cov = [[1, 0], [0, 1]]\n        >>> rng = np.random.default_rng()\n        >>> x = rng.multivariate_normal(mean, cov, (3, 3))\n        >>> x.shape\n        (3, 3, 2)\n\n        We can use a different method other than the default to factorize cov:\n        >>> y = rng.multivariate_normal(mean, cov, (3, 3), method='cholesky')\n        >>> y.shape\n        (3, 3, 2)\n\n        The following is probably true, given that 0.6 is roughly twice the\n        standard deviation:\n\n        >>> list((x[0,0,:] - mean) < 0.6)\n        [True, True] # random\n\n        ";
static const char __pyx_k_negative_binomial_n_p_size_None[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative binomial distribution.\n\n        Samples are drawn from a negative binomial distribution with specified\n        parameters, `n` successes and `p` probability of success where `n`\n        is > 0 and `p` is in the interval (0, 1].\n\n        Parameters\n        ----------\n        n : float or array_like of floats\n            Parameter of the distribution, > 0.\n        p : float or array_like of floats\n            Parameter of the distribution. Must satisfy 0 < p <= 1.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``n`` and ``p`` are both scalars.\n            Otherwise, ``np.broadcast(n, p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized negative binomial distribution,\n            where each sample is equal to N, the number of failures that\n            occurred before a total of n successes was reached.\n\n        Notes\n        -----\n        The probability mass function of the negative binomial distribution is\n\n        .. math:: P(N;n,p) = \\frac{\\Gamma(N+n)}{N!\\Gamma(n)}p^{n}(1-p)^{N},\n\n        where :math:`n` is the number of successes, :math:`p` is the\n        probability of success, :math:`N+n` is the number of trials, and\n        :math:`\\Gamma` is the gamma function. When :math:`n` is an integer,\n        :math:`\\frac{\\Gamma(N+n)}{N!\\Gamma(n)} = \\binom{N+n-1}{N}`, which is\n        the more common form of this term in the the pmf. The negative\n        binomial distribution gives the probability of N failures given n\n        successes, with a success on the last trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears,\n        then th""e probability distribution of the number of non-\"1\"s that\n        appear before the third \"1\" is a negative binomial distribution.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n        .. [2] Wikipedia, \"Negative binomial distribution\",\n               https://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        A real world example. A company drills wild-cat oil\n        exploration wells, each with an estimated probability of\n        success of 0.1.  What is the probability of having one success\n        for each successive well, that is what is the probability of a\n        single success after drilling 5 wells, after 6 wells, etc.?\n\n        >>> s = np.random.default_rng().negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11): # doctest: +SKIP\n        ...    probability = sum(s<i) / 100000.\n        ...    print(i, \"wells drilled, probability of one success =\", probability)\n\n        ";
static const char __pyx_k_noncentral_chisquare_df_nonc_si[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalization of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : float or array_like of floats\n            Degrees of freedom, must be > 0.\n\n            .. versionchanged:: 1.10.0\n               Earlier NumPy versions required dfnum > 1.\n        nonc : float or array_like of floats\n            Non-centrality, must be non-negative.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``df`` and ``nonc`` are both scalars.\n            Otherwise, ``np.broadcast(df, nonc).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized noncentral chi-square distribution.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square\n        distribution is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}\n                               P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        References\n        ----------\n        .. [1] Wikipedia, \"Noncentral chi-squared distribution\"\n               https://en.wikipedia.org/wiki/Noncentral_chi-squared_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> rng = np.random.default_rng()\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(rng.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, density=True)\n        >>> plt.show()""\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(rng.noncentral_chisquare(3, .0000001, 100000),\n        ...                   bins=np.arange(0., 25, .1), density=True)\n        >>> values2 = plt.hist(rng.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), density=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(rng.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, density=True)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_noncentral_f_dfnum_dfden_nonc_s[] = "\n        noncentral_f(dfnum, dfden, nonc, size=None)\n\n        Draw samples from the noncentral F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n        freedom in denominator), where both parameters > 1.\n        `nonc` is the non-centrality parameter.\n\n        Parameters\n        ----------\n        dfnum : float or array_like of floats\n            Numerator degrees of freedom, must be > 0.\n\n            .. versionchanged:: 1.14.0\n               Earlier NumPy versions required dfnum > 1.\n        dfden : float or array_like of floats\n            Denominator degrees of freedom, must be > 0.\n        nonc : float or array_like of floats\n            Non-centrality parameter, the sum of the squares of the numerator\n            means, must be >= 0.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``dfnum``, ``dfden``, and ``nonc``\n            are all scalars.  Otherwise, ``np.broadcast(dfnum, dfden, nonc).size``\n            samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized noncentral Fisher distribution.\n\n        Notes\n        -----\n        When calculating the power of an experiment (power = probability of\n        rejecting the null hypothesis when a specific alternative is true) the\n        non-central F statistic becomes important.  When the null hypothesis is\n        true, the F statistic follows a central F distribution. When the null\n        hypothesis is not true, then it follows a non-central F statistic.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Noncentral F-Distribution.\"\n               From MathWorld--""A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NoncentralF-Distribution.html\n        .. [2] Wikipedia, \"Noncentral F-distribution\",\n               https://en.wikipedia.org/wiki/Noncentral_F-distribution\n\n        Examples\n        --------\n        In a study, testing for a specific alternative to the null hypothesis\n        requires use of the Noncentral F distribution. We need to calculate the\n        area in the tail of the distribution that exceeds the value of the F\n        distribution for the null hypothesis.  We'll plot the two probability\n        distributions for comparison.\n\n        >>> rng = np.random.default_rng()\n        >>> dfnum = 3 # between group deg of freedom\n        >>> dfden = 20 # within groups degrees of freedom\n        >>> nonc = 3.0\n        >>> nc_vals = rng.noncentral_f(dfnum, dfden, nonc, 1000000)\n        >>> NF = np.histogram(nc_vals, bins=50, density=True)\n        >>> c_vals = rng.f(dfnum, dfden, 1000000)\n        >>> F = np.histogram(c_vals, bins=50, density=True)\n        >>> import matplotlib.pyplot as plt\n        >>> plt.plot(F[1][1:], F[0])\n        >>> plt.plot(NF[1][1:], NF[0])\n        >>> plt.show()\n\n        ";
static const char __pyx_k_normal_loc_0_0_scale_1_0_size_N[] = "\n        normal(loc=0.0, scale=1.0, size=None)\n\n        Draw random samples from a normal (Gaussian) distribution.\n\n        The probability density function of the normal distribution, first\n        derived by De Moivre and 200 years later by both Gauss and Laplace\n        independently [2]_, is often called the bell curve because of\n        its characteristic shape (see the example below).\n\n        The normal distributions occurs often in nature.  For example, it\n        describes the commonly occurring distribution of samples influenced\n        by a large number of tiny, random disturbances, each with its own\n        unique distribution [2]_.\n\n        Parameters\n        ----------\n        loc : float or array_like of floats\n            Mean (\"centre\") of the distribution.\n        scale : float or array_like of floats\n            Standard deviation (spread or \"width\") of the distribution. Must be\n            non-negative.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized normal distribution.\n\n        See Also\n        --------\n        scipy.stats.norm : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gaussian distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` the standard\n        deviation. The square of the standard deviation, :math:`""\\sigma^2`,\n        is called the variance.\n\n        The function has its peak at the mean, and its \"spread\" increases with\n        the standard deviation (the function reaches 0.607 times its maximum at\n        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n        :meth:`normal` is more likely to return samples lying close to the\n        mean, rather than those far away.\n\n        References\n        ----------\n        .. [1] Wikipedia, \"Normal distribution\",\n               https://en.wikipedia.org/wiki/Normal_distribution\n        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability,\n               Random Variables and Random Signal Principles\", 4th ed., 2001,\n               pp. 51, 51, 125.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n        >>> s = np.random.default_rng().normal(mu, sigma, 1000)\n\n        Verify the mean and the variance:\n\n        >>> abs(mu - np.mean(s))\n        0.0  # may vary\n\n        >>> abs(sigma - np.std(s, ddof=1))\n        0.1  # may vary\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, density=True)\n        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Two-by-four array of samples from N(3, 6.25):\n\n        >>> np.random.default_rng().normal(3, 2.5, size=(2, 4))\n        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random\n               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random\n\n        ";
static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import";
static const char __pyx_k_pareto_a_size_None_Draw_samples[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with\n        specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto\n        distribution. The classical Pareto distribution can be\n        obtained from the Lomax distribution by adding 1 and\n        multiplying by the scale parameter ``m`` (see Notes).  The\n        smallest value of the Lomax distribution is zero while for the\n        classical Pareto distribution it is ``mu``, where the standard\n        Pareto distribution has location ``mu = 1``.  Lomax can also\n        be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set\n        to one and the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is\n        unbounded above.  It is also known as the \"80-20 rule\".  In\n        this distribution, 80 percent of the weights are in the lowest\n        20 percent of the range, while the other 20 percent fill the\n        remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        a : float or array_like of floats\n            Shape of the distribution. Must be positive.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar.  Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Pareto distribution.\n\n        See Also\n        --------\n        scipy.stats.lomax : probability density function, distribution or\n            cumulative density function, etc.\n        scipy.stats.genpareto : probability density function, distribution or\n            cumulative density function,"" etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the scale.\n\n        The Pareto distribution, named after the Italian economist\n        Vilfredo Pareto, is a power law probability distribution\n        useful in many real world problems.  Outside the field of\n        economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe\n        the distribution of wealth in an economy.  It has also found\n        use in insurance, web page access statistics, oil field sizes,\n        and many other problems, including the download frequency for\n        projects in Sourceforge [1]_.  It is one of the so-called\n        \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               https://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 2.  # shape and mode\n        >>> s = (np.random.default_rng().pareto(a, 1000) + 1) * m\n\n        Display the histogram of the samples, along with the probability\n        density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, _ = plt.hist(s, 100, density=True)\n        >>> fit = a*m**a / bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
static const char __pyx_k_permutation_x_axis_0_Randomly_p[] = "\n        permutation(x, axis=0)\n\n        Randomly permute a sequence, or return a permuted range.\n\n        Parameters\n        ----------\n        x : int or array_like\n            If `x` is an integer, randomly permute ``np.arange(x)``.\n            If `x` is an array, make a copy and shuffle the elements\n            randomly.\n        axis : int, optional\n            The axis which `x` is shuffled along. Default is 0.\n\n        Returns\n        -------\n        out : ndarray\n            Permuted sequence or array range.\n\n        Examples\n        --------\n        >>> rng = np.random.default_rng()\n        >>> rng.permutation(10)\n        array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) # random\n\n        >>> rng.permutation([1, 4, 9, 12, 15])\n        array([15,  1,  9,  4, 12]) # random\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> rng.permutation(arr)\n        array([[6, 7, 8], # random\n               [0, 1, 2],\n               [3, 4, 5]])\n\n        >>> rng.permutation(\"abc\")\n        Traceback (most recent call last):\n            ...\n        numpy.AxisError: axis 0 is out of bounds for array of dimension 0\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> rng.permutation(arr, axis=1)\n        array([[0, 2, 1], # random\n               [3, 5, 4],\n               [6, 8, 7]])\n\n        ";
static const char __pyx_k_power_a_size_None_Draws_samples[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float or array_like of floats\n            Parameter of the distribution. Must be non-negative.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar.  Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized power distribution.\n\n        Raises\n        ------\n        ValueError\n            If a < 1.\n\n        Notes\n        -----\n        The probability density function is\n\n        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n        The power function distribution is just the inverse of the Pareto\n        distribution. It may also be seen as a special case of the Beta\n        distribution.\n\n        It is used, for example, in modeling the over-reporting of insurance\n        claims.\n\n        References\n        ----------\n        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n               in economics and actuarial sciences\", Wiley, 2003.\n        .. [2] Heckert, N. A. and Filliben, James J. \"NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology\n               Handbook Series, June 2003.\n               https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> rng = np.random.default_rng()\n        >>> a = ""5. # shape\n        >>> samples = 1000\n        >>> s = rng.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats  # doctest: +SKIP\n        >>> rvs = rng.power(5, 1000000)\n        >>> rvsp = rng.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)  # doctest: +SKIP\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, density=True)\n        >>> plt.plot(xx,powpdf,'r-')  # doctest: +SKIP\n        >>> plt.title('power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, density=True)\n        >>> plt.plot(xx,powpdf,'r-')  # doctest: +SKIP\n        >>> plt.title('inverse of 1 + Generator.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, density=True)\n        >>> plt.plot(xx,powpdf,'r-')  # doctest: +SKIP\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
static const char __pyx_k_random_size_None_dtype_np_float[] = "\n        random(size=None, dtype=np.float64, out=None)\n\n        Return random floats in the half-open interval [0.0, 1.0).\n\n        Results are from the \"continuous uniform\" distribution over the\n        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n        the output of `random` by `(b-a)` and add `a`::\n\n          (b - a) * random() + a\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\n        dtype : dtype, optional\n            Desired dtype of the result, only `float64` and `float32` are supported.\n            Byteorder must be native. The default value is np.float64.\n        out : ndarray, optional\n            Alternative output array in which to place the result. If size is not None,\n            it must have the same shape as the provided size and must match the type of\n            the output values.\n\n        Returns\n        -------\n        out : float or ndarray of floats\n            Array of random floats of shape `size` (unless ``size=None``, in which\n            case a single float is returned).\n\n        Examples\n        --------\n        >>> rng = np.random.default_rng()\n        >>> rng.random()\n        0.47108547995356098 # random\n        >>> type(rng.random())\n        <class 'float'>\n        >>> rng.random((5,))\n        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428]) # random\n\n        Three-by-two array of random numbers from [-5, 0):\n\n        >>> 5 * rng.random((3, 2)) - 5\n        array([[-3.99149989, -0.52338984], # random\n               [-2.99091858, -0.79479508],\n               [-1.23204345, -1.75224494]])\n\n        ";
static const char __pyx_k_rayleigh_scale_1_0_size_None_Dr[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : float or array_like of floats, optional\n            Scale, also equals the mode. Must be non-negative. Default is 1.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``scale`` is a scalar.  Otherwise,\n            ``np.array(scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Rayleigh distribution.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution would arise, for example, if the East\n        and North components of the wind velocity had identical zero-mean\n        Gaussian distributions.  Then the wind speed would have a Rayleigh\n        distribution.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., \"Rayleigh Distribution,\"\n               https://web.archive.org/web/20090514091424/http://brighton-webs.co.uk:80/distributions/rayleigh.asp\n        .. [2] Wikipedia, \"Rayleigh distribution\"\n               https://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> from matplotlib.pyplot import hist\n        >>> rng = np.random.default_rng()\n        >>> values = hist(rng.rayleigh(3, 100000), bins=200, density=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n       "" height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = rng.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003 # random\n\n        ";
static const char __pyx_k_shuffle_x_axis_0_Modify_a_seque[] = "\n        shuffle(x, axis=0)\n\n        Modify a sequence in-place by shuffling its contents.\n\n        The order of sub-arrays is changed but their contents remains the same.\n\n        Parameters\n        ----------\n        x : array_like\n            The array or list to be shuffled.\n        axis : int, optional\n            The axis which `x` is shuffled along. Default is 0.\n            It is only supported on `ndarray` objects.\n\n        Returns\n        -------\n        None\n\n        Examples\n        --------\n        >>> rng = np.random.default_rng()\n        >>> arr = np.arange(10)\n        >>> rng.shuffle(arr)\n        >>> arr\n        [1 7 5 2 9 4 3 6 0 8] # random\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> rng.shuffle(arr)\n        >>> arr\n        array([[3, 4, 5], # random\n               [6, 7, 8],\n               [0, 1, 2]])\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> rng.shuffle(arr, axis=1)\n        >>> arr\n        array([[2, 0, 1], # random\n               [5, 3, 4],\n               [8, 6, 7]])\n        ";
static const char __pyx_k_standard_normal_size_None_dtype[] = "\n        standard_normal(size=None, dtype=np.float64, out=None)\n\n        Draw samples from a standard Normal distribution (mean=0, stdev=1).\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\n        dtype : dtype, optional\n            Desired dtype of the result, only `float64` and `float32` are supported.\n            Byteorder must be native. The default value is np.float64.\n        out : ndarray, optional\n            Alternative output array in which to place the result. If size is not None,\n            it must have the same shape as the provided size and must match the type of\n            the output values.\n\n        Returns\n        -------\n        out : float or ndarray\n            A floating-point array of shape ``size`` of drawn samples, or a\n            single sample if ``size`` was not specified.\n\n        See Also\n        --------\n        normal :\n            Equivalent function with additional ``loc`` and ``scale`` arguments\n            for setting the mean and standard deviation.\n\n        Notes\n        -----\n        For random samples from :math:`N(\\mu, \\sigma^2)`, use one of::\n\n            mu + sigma * gen.standard_normal(size=...)\n            gen.normal(mu, sigma, size=...)\n\n        Examples\n        --------\n        >>> rng = np.random.default_rng()\n        >>> rng.standard_normal()\n        2.1923875335537315 #random\n\n        >>> s = rng.standard_normal(8000)\n        >>> s\n        array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random\n               -0.38672696, -0.4685006 ])                                # random\n        >>> s.shape\n        (8000,)\n        >>> s = rng.standard_normal(size=(3, 4, 2))\n        >>> s.shape\n        (3, 4, 2)\n\n        Two-by-fou""r array of samples from :math:`N(3, 6.25)`:\n\n        >>> 3 + 2.5 * rng.standard_normal(size=(2, 4))\n        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random\n               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random\n\n        ";
static const char __pyx_k_standard_t_df_size_None_Draw_sa[] = "\n        standard_t(df, size=None)\n\n        Draw samples from a standard Student's t distribution with `df` degrees\n        of freedom.\n\n        A special case of the hyperbolic distribution.  As `df` gets\n        large, the result resembles that of the standard normal\n        distribution (`standard_normal`).\n\n        Parameters\n        ----------\n        df : float or array_like of floats\n            Degrees of freedom, must be > 0.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``df`` is a scalar.  Otherwise,\n            ``np.array(df).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized standard Student's t distribution.\n\n        Notes\n        -----\n        The probability density function for the t distribution is\n\n        .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n                  \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n        The t test is based on an assumption that the data come from a\n        Normal distribution. The t test provides a way to test whether\n        the sample mean (that is the mean calculated from the data) is\n        a good estimate of the true mean.\n\n        The derivation of the t-distribution was first published in\n        1908 by William Gosset while working for the Guinness Brewery\n        in Dublin. Due to proprietary issues, he had to publish under\n        a pseudonym, and so he used the name Student.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n               Springer, 2002.\n        .. [2] Wikipedia, \"Student's t-distribution\"\n               https://en.wikipedia.org/wiki/Student's_t-distribu""tion\n\n        Examples\n        --------\n        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n        women in kilojoules (kJ) is:\n\n        >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n        ...                    7515, 8230, 8770])\n\n        Does their energy intake deviate systematically from the recommended\n        value of 7725 kJ?\n\n        We have 10 degrees of freedom, so is the sample mean within 95% of the\n        recommended value?\n\n        >>> s = np.random.default_rng().standard_t(10, size=100000)\n        >>> np.mean(intake)\n        6753.636363636364\n        >>> intake.std(ddof=1)\n        1142.1232221373727\n\n        Calculate the t statistic, setting the ddof parameter to the unbiased\n        value so the divisor in the standard deviation will be degrees of\n        freedom, N-1.\n\n        >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(s, bins=100, density=True)\n\n        For a one-sided t-test, how far out in the distribution does the t\n        statistic appear?\n\n        >>> np.sum(s<t) / float(len(s))\n        0.0090699999999999999  #random\n\n        So the p-value is about 0.009, which says the null hypothesis has a\n        probability of about 99% of being true.\n\n        ";
static const char __pyx_k_triangular_left_mode_right_size[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution over the\n        interval ``[left, right]``.\n\n        The triangular distribution is a continuous probability\n        distribution with lower limit left, peak at mode, and upper\n        limit right. Unlike the other distributions, these parameters\n        directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : float or array_like of floats\n            Lower limit.\n        mode : float or array_like of floats\n            The value where the peak of the distribution occurs.\n            The value must fulfill the condition ``left <= mode <= right``.\n        right : float or array_like of floats\n            Upper limit, must be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``left``, ``mode``, and ``right``\n            are all scalars.  Otherwise, ``np.broadcast(left, mode, right).size``\n            samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized triangular distribution.\n\n        Notes\n        -----\n        The probability density function for the triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(r-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined\n        problems where the underlying distribution is not known, but\n        some knowledge of the limits and mode exists. Often it is used\n        in simulations.\n\n        Ref""erences\n        ----------\n        .. [1] Wikipedia, \"Triangular distribution\"\n               https://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.default_rng().triangular(-3, 0, 8, 100000), bins=200,\n        ...              density=True)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_uniform_low_0_0_high_1_0_size_N[] = "\n        uniform(low=0.0, high=1.0, size=None)\n\n        Draw samples from a uniform distribution.\n\n        Samples are uniformly distributed over the half-open interval\n        ``[low, high)`` (includes low, but excludes high).  In other words,\n        any value within the given interval is equally likely to be drawn\n        by `uniform`.\n\n        Parameters\n        ----------\n        low : float or array_like of floats, optional\n            Lower boundary of the output interval.  All values generated will be\n            greater than or equal to low.  The default value is 0.\n        high : float or array_like of floats\n            Upper boundary of the output interval.  All values generated will be\n            less than high.  The default value is 1.0.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``low`` and ``high`` are both scalars.\n            Otherwise, ``np.broadcast(low, high).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized uniform distribution.\n\n        See Also\n        --------\n        integers : Discrete uniform distribution, yielding integers.\n        random : Floats uniformly distributed over ``[0, 1)``.\n\n        Notes\n        -----\n        The probability density function of the uniform distribution is\n\n        .. math:: p(x) = \\frac{1}{b - a}\n\n        anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n        When ``high`` == ``low``, values of ``low`` will be returned.\n        If ``high`` < ``low``, the results are officially undefined\n        and may eventually raise an error, i.e. do not rely on this\n        function to behave when passed arguments satisfying that\n        inequality condition.\n\n        Exampl""es\n        --------\n        Draw samples from the distribution:\n\n        >>> s = np.random.default_rng().uniform(-1,0,1000)\n\n        All values are within the given interval:\n\n        >>> np.all(s >= -1)\n        True\n        >>> np.all(s < 0)\n        True\n\n        Display the histogram of the samples, along with the\n        probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 15, density=True)\n        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
static const char __pyx_k_vonmises_mu_kappa_size_None_Dra[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float or array_like of floats\n            Mode (\"center\") of the distribution.\n        kappa : float or array_like of floats\n            Dispersion of the distribution, has to be >=0.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``mu`` and ``kappa`` are both scalars.\n            Otherwise, ``np.broadcast(mu, kappa).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized von Mises distribution.\n\n        See Also\n        --------\n        scipy.stats.vonmises : probability density function, distribution, or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the von Mises distribution is\n\n        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n        The von Mises is named for Richard Edler von Mises, who was born in\n        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n        States in 1939 and became a professor at Harvard.  He worked in\n        probability theory, aero""dynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). \"Handbook of\n               Mathematical Functions with Formulas, Graphs, and Mathematical\n               Tables, 9th printing,\" New York: Dover, 1972.\n        .. [2] von Mises, R., \"Mathematical Theory of Probability\n               and Statistics\", New York: Academic Press, 1964.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n        >>> s = np.random.default_rng().vonmises(mu, kappa, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> from scipy.special import i0  # doctest: +SKIP\n        >>> plt.hist(s, 50, density=True)\n        >>> x = np.linspace(-np.pi, np.pi, num=51)\n        >>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa))  # doctest: +SKIP\n        >>> plt.plot(x, y, linewidth=2, color='r')  # doctest: +SKIP\n        >>> plt.show()\n\n        ";
static const char __pyx_k_weibull_a_size_None_Draw_sample[] = "\n        weibull(a, size=None)\n\n        Draw samples from a Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float or array_like of floats\n            Shape parameter of the distribution.  Must be nonnegative.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar.  Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Weibull distribution.\n\n        See Also\n        --------\n        scipy.stats.weibull_max\n        scipy.stats.weibull_min\n        scipy.stats.genextreme\n        gumbel\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution\n        for smallest values, SEV Type III, or Rosin-Rammler\n        distribution) is one of a class of Generalized Extreme Value\n        (GEV) distributions used in modeling extreme value problems.\n        This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull"" distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, \"A Statistical Distribution Function of\n               Wide Applicability\", Journal Of Applied Mechanics ASME Paper\n               1951.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               https://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> rng = np.random.default_rng()\n        >>> a = 5. # shape\n        >>> s = rng.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(rng.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_zipf_a_size_None_Draw_samples_f[] = "\n        zipf(a, size=None)\n\n        Draw samples from a Zipf distribution.\n\n        Samples are drawn from a Zipf distribution with specified parameter\n        `a` > 1.\n\n        The Zipf distribution (also known as the zeta distribution) is a\n        continuous probability distribution that satisfies Zipf's law: the\n        frequency of an item is inversely proportional to its rank in a\n        frequency table.\n\n        Parameters\n        ----------\n        a : float or array_like of floats\n            Distribution parameter. Must be greater than 1.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar. Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Zipf distribution.\n\n        See Also\n        --------\n        scipy.stats.zipf : probability density function, distribution, or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Zipf distribution is\n\n        .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n        where :math:`\\zeta` is the Riemann Zeta function.\n\n        It is named for the American linguist George Kingsley Zipf, who noted\n        that the frequency of any word in a sample of a language is inversely\n        proportional to its rank in the frequency table.\n\n        References\n        ----------\n        .. [1] Zipf, G. K., \"Selected Studies of the Principle of Relative\n               Frequency in Language,\" Cambridge, MA: Harvard Univ. Press,\n               1932.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 2. # parameter\n        >>> s = np.random.default_rn""g().zipf(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> from scipy import special  # doctest: +SKIP\n\n        Truncate s values at 50 so plot is interesting:\n\n        >>> count, bins, ignored = plt.hist(s[s<50],\n        ...         50, density=True)\n        >>> x = np.arange(1., 50.)\n        >>> y = x**(-a) / special.zetac(a)  # doctest: +SKIP\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')  # doctest: +SKIP\n        >>> plt.show()\n\n        ";
static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
static const char __pyx_k_Cannot_take_a_larger_sample_than[] = "Cannot take a larger sample than population when replace is False";
static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
static const char __pyx_k_Fewer_non_zero_entries_in_p_than[] = "Fewer non-zero entries in p than size";
static const char __pyx_k_Generator_hypergeometric_line_31[] = "Generator.hypergeometric (line 3120)";
static const char __pyx_k_Generator_multivariate_hypergeom[] = "Generator.multivariate_hypergeometric (line 3699)";
static const char __pyx_k_Generator_multivariate_normal_li[] = "Generator.multivariate_normal (line 3344)";
static const char __pyx_k_Generator_negative_binomial_line[] = "Generator.negative_binomial (line 2841)";
static const char __pyx_k_Generator_noncentral_chisquare_l[] = "Generator.noncentral_chisquare (line 1444)";
static const char __pyx_k_Generator_noncentral_f_line_1298[] = "Generator.noncentral_f (line 1298)";
static const char __pyx_k_Generator_standard_cauchy_line_1[] = "Generator.standard_cauchy (line 1524)";
static const char __pyx_k_Generator_standard_exponential_l[] = "Generator.standard_exponential (line 309)";
static const char __pyx_k_Generator_standard_gamma_line_10[] = "Generator.standard_gamma (line 1041)";
static const char __pyx_k_Generator_standard_normal_line_8[] = "Generator.standard_normal (line 869)";
static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))";
static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
static const char __pyx_k_Insufficient_memory_for_multivar[] = "Insufficient memory for multivariate_hypergeometric with method='count' and sum(colors)=%d";
static const char __pyx_k_Invalid_bit_generator_The_bit_ge[] = "Invalid bit generator. The bit generator must be instantiated.";
static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)";
static const char __pyx_k_Providing_a_dtype_with_a_non_nat[] = "Providing a dtype with a non-native byteorder is not supported. If you require platform-independent byteorder, call byteswap when required.";
static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
static const char __pyx_k_Unsupported_dtype_r_for_integers[] = "Unsupported dtype %r for integers";
static const char __pyx_k_Unsupported_dtype_r_for_standard[] = "Unsupported dtype %r for standard_exponential";
static const char __pyx_k_When_method_is_marginals_sum_col[] = "When method is \"marginals\", sum(colors) must be less than 1000000000.";
static const char __pyx_k_a_cannot_be_empty_unless_no_samp[] = "a cannot be empty unless no samples aretaken";
static const char __pyx_k_a_must_be_a_positive_integer_unl[] = "a must be a positive integer unless nosamples are taken";
static const char __pyx_k_both_ngood_and_nbad_must_be_less[] = "both ngood and nbad must be less than %d";
static const char __pyx_k_check_valid_must_equal_warn_rais[] = "check_valid must equal 'warn', 'raise', or 'ignore'";
static const char __pyx_k_colors_must_be_a_one_dimensional[] = "colors must be a one-dimensional sequence of nonnegative integers not exceeding %d.";
static const char __pyx_k_cov_must_be_2_dimensional_and_sq[] = "cov must be 2 dimensional and square";
static const char __pyx_k_covariance_is_not_positive_semid[] = "covariance is not positive-semidefinite.";
static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
static const char __pyx_k_mean_and_cov_must_have_same_leng[] = "mean and cov must have same length";
static const char __pyx_k_method_must_be_count_or_marginal[] = "method must be \"count\" or \"marginals\".";
static const char __pyx_k_method_must_be_one_of_eigh_svd_c[] = "method must be one of {'eigh', 'svd', 'cholesky'}";
static const char __pyx_k_negative_dimensions_are_not_allo[] = "negative dimensions are not allowed";
static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import";
static const char __pyx_k_probabilities_are_not_non_negati[] = "probabilities are not non-negative";
static const char __pyx_k_sum_colors_must_not_exceed_the_m[] = "sum(colors) must not exceed the maximum value of a 64 bit signed integer (%d)";
static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
static const char __pyx_k_Unsupported_dtype_r_for_standard_2[] = "Unsupported dtype %r for standard_normal";
static const char __pyx_k_Unsupported_dtype_r_for_standard_3[] = "Unsupported dtype %r for standard_gamma";
static PyObject *__pyx_n_s_ASCII;
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static PyObject *__pyx_n_s_Ellipsis;
static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
static PyObject *__pyx_kp_u_Fewer_non_zero_entries_in_p_than;
static PyObject *__pyx_n_s_Generator;
static PyObject *__pyx_kp_u_Generator_binomial_line_2697;
static PyObject *__pyx_kp_u_Generator_bytes_line_489;
static PyObject *__pyx_kp_u_Generator_chisquare_line_1376;
static PyObject *__pyx_kp_u_Generator_choice_line_518;
static PyObject *__pyx_kp_u_Generator_dirichlet_line_3915;
static PyObject *__pyx_kp_u_Generator_f_line_1210;
static PyObject *__pyx_kp_u_Generator_gamma_line_1132;
static PyObject *__pyx_kp_u_Generator_geometric_line_3069;
static PyObject *__pyx_kp_u_Generator_gumbel_line_2149;
static PyObject *__pyx_kp_u_Generator_hypergeometric_line_31;
static PyObject *__pyx_kp_u_Generator_integers_line_362;
static PyObject *__pyx_kp_u_Generator_laplace_line_2064;
static PyObject *__pyx_kp_u_Generator_logistic_line_2268;
static PyObject *__pyx_kp_u_Generator_lognormal_line_2348;
static PyObject *__pyx_kp_u_Generator_logseries_line_3263;
static PyObject *__pyx_kp_u_Generator_multinomial_line_3548;
static PyObject *__pyx_kp_u_Generator_multivariate_hypergeom;
static PyObject *__pyx_kp_u_Generator_multivariate_normal_li;
static PyObject *__pyx_kp_u_Generator_negative_binomial_line;
static PyObject *__pyx_kp_u_Generator_noncentral_chisquare_l;
static PyObject *__pyx_kp_u_Generator_noncentral_f_line_1298;
static PyObject *__pyx_kp_u_Generator_normal_line_939;
static PyObject *__pyx_kp_u_Generator_pareto_line_1766;
static PyObject *__pyx_kp_u_Generator_permutation_line_4256;
static PyObject *__pyx_kp_u_Generator_poisson_line_2917;
static PyObject *__pyx_kp_u_Generator_power_line_1963;
static PyObject *__pyx_kp_u_Generator_random_line_155;
static PyObject *__pyx_kp_u_Generator_rayleigh_line_2460;
static PyObject *__pyx_kp_u_Generator_shuffle_line_4114;
static PyObject *__pyx_kp_u_Generator_standard_cauchy_line_1;
static PyObject *__pyx_kp_u_Generator_standard_exponential_l;
static PyObject *__pyx_kp_u_Generator_standard_gamma_line_10;
static PyObject *__pyx_kp_u_Generator_standard_normal_line_8;
static PyObject *__pyx_kp_u_Generator_standard_t_line_1589;
static PyObject *__pyx_kp_u_Generator_triangular_line_2597;
static PyObject *__pyx_kp_u_Generator_uniform_line_762;
static PyObject *__pyx_kp_u_Generator_vonmises_line_1683;
static PyObject *__pyx_kp_u_Generator_wald_line_2529;
static PyObject *__pyx_kp_u_Generator_weibull_line_1864;
static PyObject *__pyx_kp_u_Generator_zipf_line_2989;
static PyObject *__pyx_n_s_ImportError;
static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0;
static PyObject *__pyx_n_s_IndexError;
static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
static PyObject *__pyx_kp_u_Insufficient_memory_for_multivar;
static PyObject *__pyx_kp_u_Invalid_bit_generator_The_bit_ge;
static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;
static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;
static PyObject *__pyx_n_s_MemoryError;
static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
static PyObject *__pyx_kp_s_MemoryView_of_r_object;
static PyObject *__pyx_n_s_NotImplementedError;
static PyObject *__pyx_n_b_O;
static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
static PyObject *__pyx_n_s_OverflowError;
static PyObject *__pyx_n_s_PCG64;
static PyObject *__pyx_n_s_PickleError;
static PyObject *__pyx_kp_u_Providing_a_dtype_with_a_non_nat;
static PyObject *__pyx_kp_u_Range_exceeds_valid_bounds;
static PyObject *__pyx_n_s_RuntimeWarning;
static PyObject *__pyx_n_s_T;
static PyObject *__pyx_n_s_TypeError;
static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
static PyObject *__pyx_kp_u_Unsupported_dtype_r_for_integers;
static PyObject *__pyx_kp_u_Unsupported_dtype_r_for_random;
static PyObject *__pyx_kp_u_Unsupported_dtype_r_for_standard;
static PyObject *__pyx_kp_u_Unsupported_dtype_r_for_standard_2;
static PyObject *__pyx_kp_u_Unsupported_dtype_r_for_standard_3;
static PyObject *__pyx_n_s_ValueError;
static PyObject *__pyx_n_s_View_MemoryView;
static PyObject *__pyx_kp_u_When_method_is_count_sum_colors;
static PyObject *__pyx_kp_u_When_method_is_marginals_sum_col;
static PyObject *__pyx_kp_u__2;
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static PyObject *__pyx_kp_u__5;
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static PyObject *__pyx_kp_u_a_and_p_must_have_same_size;
static PyObject *__pyx_kp_u_a_cannot_be_empty_unless_no_samp;
static PyObject *__pyx_kp_u_a_must_an_array_or_an_integer;
static PyObject *__pyx_kp_u_a_must_be_a_positive_integer_unl;
static PyObject *__pyx_n_s_add;
static PyObject *__pyx_n_s_all;
static PyObject *__pyx_n_s_allclose;
static PyObject *__pyx_n_s_allocate_buffer;
static PyObject *__pyx_n_s_alpha;
static PyObject *__pyx_kp_u_alpha_0;
static PyObject *__pyx_n_s_any;
static PyObject *__pyx_n_s_arange;
static PyObject *__pyx_n_s_array;
static PyObject *__pyx_n_s_asarray;
static PyObject *__pyx_n_s_ascontiguousarray;
static PyObject *__pyx_n_s_astype;
static PyObject *__pyx_kp_u_at_0x_X;
static PyObject *__pyx_n_s_atol;
static PyObject *__pyx_n_s_axis;
static PyObject *__pyx_n_s_b;
static PyObject *__pyx_n_u_b;
static PyObject *__pyx_n_s_base;
static PyObject *__pyx_kp_u_binomial_n_p_size_None_Draw_sam;
static PyObject *__pyx_n_s_bit_generator;
static PyObject *__pyx_n_u_bit_generator;
static PyObject *__pyx_n_s_bool;
static PyObject *__pyx_kp_u_both_ngood_and_nbad_must_be_less;
static PyObject *__pyx_kp_u_bytes_length_Return_random_byte;
static PyObject *__pyx_n_s_c;
static PyObject *__pyx_n_u_c;
static PyObject *__pyx_n_s_capsule;
static PyObject *__pyx_n_u_capsule;
static PyObject *__pyx_n_s_check_valid;
static PyObject *__pyx_kp_u_check_valid_must_equal_warn_rais;
static PyObject *__pyx_kp_u_chisquare_df_size_None_Draw_sam;
static PyObject *__pyx_kp_u_choice_a_size_None_replace_True;
static PyObject *__pyx_n_s_cholesky;
static PyObject *__pyx_n_u_cholesky;
static PyObject *__pyx_n_s_class;
static PyObject *__pyx_n_s_cline_in_traceback;
static PyObject *__pyx_n_s_colors;
static PyObject *__pyx_kp_u_colors_must_be_a_one_dimensional;
static PyObject *__pyx_n_s_compat;
static PyObject *__pyx_kp_s_contiguous_and_direct;
static PyObject *__pyx_kp_s_contiguous_and_indirect;
static PyObject *__pyx_n_s_copy;
static PyObject *__pyx_n_u_count;
static PyObject *__pyx_n_s_count_nonzero;
static PyObject *__pyx_n_s_cov;
static PyObject *__pyx_kp_u_cov_must_be_2_dimensional_and_sq;
static PyObject *__pyx_kp_u_covariance_is_not_positive_semid;
static PyObject *__pyx_n_s_cumsum;
static PyObject *__pyx_n_s_default_rng;
static PyObject *__pyx_n_s_df;
static PyObject *__pyx_n_u_df;
static PyObject *__pyx_n_s_dfden;
static PyObject *__pyx_n_u_dfden;
static PyObject *__pyx_n_s_dfnum;
static PyObject *__pyx_n_u_dfnum;
static PyObject *__pyx_n_s_dict;
static PyObject *__pyx_kp_u_dirichlet_alpha_size_None_Draw;
static PyObject *__pyx_n_s_dot;
static PyObject *__pyx_n_s_double;
static PyObject *__pyx_n_s_dtype;
static PyObject *__pyx_n_s_dtype_is_object;
static PyObject *__pyx_n_s_eigh;
static PyObject *__pyx_n_u_eigh;
static PyObject *__pyx_n_s_empty;
static PyObject *__pyx_n_s_empty_like;
static PyObject *__pyx_n_s_encode;
static PyObject *__pyx_n_s_endpoint;
static PyObject *__pyx_n_s_enter;
static PyObject *__pyx_n_s_enumerate;
static PyObject *__pyx_n_s_eps;
static PyObject *__pyx_n_s_equal;
static PyObject *__pyx_n_s_error;
static PyObject *__pyx_n_s_exit;
static PyObject *__pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa;
static PyObject *__pyx_n_s_finfo;
static PyObject *__pyx_n_s_flags;
static PyObject *__pyx_n_s_float32;
static PyObject *__pyx_n_s_float64;
static PyObject *__pyx_n_s_floating;
static PyObject *__pyx_n_s_format;
static PyObject *__pyx_n_s_fortran;
static PyObject *__pyx_n_u_fortran;
static PyObject *__pyx_n_s_full;
static PyObject *__pyx_kp_u_gamma_shape_scale_1_0_size_None;
static PyObject *__pyx_n_s_generator_ctor;
static PyObject *__pyx_kp_s_generator_pyx;
static PyObject *__pyx_kp_u_geometric_p_size_None_Draw_samp;
static PyObject *__pyx_n_s_getstate;
static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
static PyObject *__pyx_n_s_greater;
static PyObject *__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N;
static PyObject *__pyx_n_s_high;
static PyObject *__pyx_kp_u_hypergeometric_ngood_nbad_nsamp;
static PyObject *__pyx_n_s_id;
static PyObject *__pyx_n_u_ignore;
static PyObject *__pyx_n_s_imatmul;
static PyObject *__pyx_n_s_import;
static PyObject *__pyx_n_s_index;
static PyObject *__pyx_n_s_int16;
static PyObject *__pyx_n_s_int32;
static PyObject *__pyx_n_s_int64;
static PyObject *__pyx_n_s_int8;
static PyObject *__pyx_n_s_integer;
static PyObject *__pyx_n_s_integers;
static PyObject *__pyx_kp_u_integers_low_high_None_size_Non;
static PyObject *__pyx_n_s_intp;
static PyObject *__pyx_n_s_isfinite;
static PyObject *__pyx_n_s_isnan;
static PyObject *__pyx_n_s_isnative;
static PyObject *__pyx_n_s_isscalar;
static PyObject *__pyx_n_s_issubdtype;
static PyObject *__pyx_n_s_item;
static PyObject *__pyx_n_s_itemsize;
static PyObject *__pyx_kp_s_itemsize_0_for_cython_array;
static PyObject *__pyx_n_s_kappa;
static PyObject *__pyx_n_u_kappa;
static PyObject *__pyx_n_s_lam;
static PyObject *__pyx_n_u_lam;
static PyObject *__pyx_kp_u_laplace_loc_0_0_scale_1_0_size;
static PyObject *__pyx_n_s_left;
static PyObject *__pyx_kp_u_left_mode;
static PyObject *__pyx_kp_u_left_right;
static PyObject *__pyx_n_s_less;
static PyObject *__pyx_n_s_less_equal;
static PyObject *__pyx_n_s_loc;
static PyObject *__pyx_n_u_loc;
static PyObject *__pyx_n_s_lock;
static PyObject *__pyx_n_s_logical_or;
static PyObject *__pyx_kp_u_logistic_loc_0_0_scale_1_0_size;
static PyObject *__pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si;
static PyObject *__pyx_kp_u_logseries_p_size_None_Draw_samp;
static PyObject *__pyx_n_s_long;
static PyObject *__pyx_n_s_low;
static PyObject *__pyx_n_s_main;
static PyObject *__pyx_n_u_marginals;
static PyObject *__pyx_n_s_matmul;
static PyObject *__pyx_n_s_max;
static PyObject *__pyx_n_s_may_share_memory;
static PyObject *__pyx_n_s_mean;
static PyObject *__pyx_n_u_mean;
static PyObject *__pyx_kp_u_mean_and_cov_must_have_same_leng;
static PyObject *__pyx_kp_u_mean_must_be_1_dimensional;
static PyObject *__pyx_n_s_memview;
static PyObject *__pyx_n_s_method;
static PyObject *__pyx_kp_u_method_must_be_count_or_marginal;
static PyObject *__pyx_kp_u_method_must_be_one_of_eigh_svd_c;
static PyObject *__pyx_n_s_mode;
static PyObject *__pyx_kp_u_mode_right;
static PyObject *__pyx_n_s_mu;
static PyObject *__pyx_n_u_mu;
static PyObject *__pyx_kp_u_multinomial_n_pvals_size_None_D;
static PyObject *__pyx_kp_u_multivariate_hypergeometric_col;
static PyObject *__pyx_kp_u_multivariate_normal_mean_cov_si;
static PyObject *__pyx_n_s_n;
static PyObject *__pyx_n_u_n;
static PyObject *__pyx_n_s_name;
static PyObject *__pyx_n_s_name_2;
static PyObject *__pyx_n_s_nbad;
static PyObject *__pyx_n_u_nbad;
static PyObject *__pyx_n_s_ndim;
static PyObject *__pyx_kp_u_negative_binomial_n_p_size_None;
static PyObject *__pyx_kp_u_negative_dimensions_are_not_allo;
static PyObject *__pyx_n_s_new;
static PyObject *__pyx_n_s_ngood;
static PyObject *__pyx_n_u_ngood;
static PyObject *__pyx_kp_u_ngood_nbad_nsample;
static PyObject *__pyx_kp_s_no_default___reduce___due_to_non;
static PyObject *__pyx_n_s_nonc;
static PyObject *__pyx_n_u_nonc;
static PyObject *__pyx_kp_u_noncentral_chisquare_df_nonc_si;
static PyObject *__pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s;
static PyObject *__pyx_kp_u_normal_loc_0_0_scale_1_0_size_N;
static PyObject *__pyx_n_s_normalize_axis_index;
static PyObject *__pyx_n_s_np;
static PyObject *__pyx_n_s_nsample;
static PyObject *__pyx_n_u_nsample;
static PyObject *__pyx_kp_u_nsample_must_be_an_integer;
static PyObject *__pyx_kp_u_nsample_must_be_nonnegative;
static PyObject *__pyx_kp_u_nsample_must_not_exceed_d;
static PyObject *__pyx_kp_u_nsample_sum_colors;
static PyObject *__pyx_n_s_numpy;
static PyObject *__pyx_n_s_numpy_core_multiarray;
static PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to;
static PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor;
static PyObject *__pyx_n_s_numpy_dual;
static PyObject *__pyx_n_s_numpy_random__generator;
static PyObject *__pyx_n_s_obj;
static PyObject *__pyx_n_s_operator;
static PyObject *__pyx_n_s_out;
static PyObject *__pyx_n_s_p;
static PyObject *__pyx_n_u_p;
static PyObject *__pyx_kp_u_p_must_be_1_dimensional;
static PyObject *__pyx_n_s_pack;
static PyObject *__pyx_kp_u_pareto_a_size_None_Draw_samples;
static PyObject *__pyx_n_s_pcg64;
static PyObject *__pyx_kp_u_permutation_x_axis_0_Randomly_p;
static PyObject *__pyx_n_s_pickle;
static PyObject *__pyx_n_s_pickle_2;
static PyObject *__pyx_kp_u_poisson_lam_1_0_size_None_Draw;
static PyObject *__pyx_n_s_poisson_lam_max;
static PyObject *__pyx_kp_u_power_a_size_None_Draws_samples;
static PyObject *__pyx_kp_u_probabilities_are_not_non_negati;
static PyObject *__pyx_kp_u_probabilities_contain_NaN;
static PyObject *__pyx_kp_u_probabilities_do_not_sum_to_1;
static PyObject *__pyx_n_s_prod;
static PyObject *__pyx_n_s_pvals;
static PyObject *__pyx_n_u_pvals;
static PyObject *__pyx_n_s_pyx_PickleError;
static PyObject *__pyx_n_s_pyx_checksum;
static PyObject *__pyx_n_s_pyx_getbuffer;
static PyObject *__pyx_n_s_pyx_result;
static PyObject *__pyx_n_s_pyx_state;
static PyObject *__pyx_n_s_pyx_type;
static PyObject *__pyx_n_s_pyx_unpickle_Enum;
static PyObject *__pyx_n_s_pyx_vtable;
static PyObject *__pyx_n_u_raise;
static PyObject *__pyx_n_s_random;
static PyObject *__pyx_kp_u_random_size_None_dtype_np_float;
static PyObject *__pyx_n_s_range;
static PyObject *__pyx_n_s_ravel;
static PyObject *__pyx_kp_u_rayleigh_scale_1_0_size_None_Dr;
static PyObject *__pyx_n_s_reduce;
static PyObject *__pyx_n_s_reduce_2;
static PyObject *__pyx_n_s_reduce_cython;
static PyObject *__pyx_n_s_reduce_ex;
static PyObject *__pyx_n_s_replace;
static PyObject *__pyx_n_s_reshape;
static PyObject *__pyx_n_s_return_index;
static PyObject *__pyx_n_s_reversed;
static PyObject *__pyx_n_s_right;
static PyObject *__pyx_n_u_right;
static PyObject *__pyx_n_s_rmatmul;
static PyObject *__pyx_n_s_rtol;
static PyObject *__pyx_n_s_scale;
static PyObject *__pyx_n_u_scale;
static PyObject *__pyx_n_s_searchsorted;
static PyObject *__pyx_n_s_seed;
static PyObject *__pyx_n_s_setstate;
static PyObject *__pyx_n_s_setstate_cython;
static PyObject *__pyx_n_s_shape;
static PyObject *__pyx_n_u_shape;
static PyObject *__pyx_n_s_shuffle;
static PyObject *__pyx_kp_u_shuffle_x_axis_0_Modify_a_seque;
static PyObject *__pyx_n_s_side;
static PyObject *__pyx_n_s_sigma;
static PyObject *__pyx_n_u_sigma;
static PyObject *__pyx_n_s_size;
static PyObject *__pyx_n_s_sort;
static PyObject *__pyx_n_s_sqrt;
static PyObject *__pyx_kp_u_standard_cauchy_size_None_Draw;
static PyObject *__pyx_kp_u_standard_exponential_size_None;
static PyObject *__pyx_kp_u_standard_gamma_shape_size_None;
static PyObject *__pyx_n_s_standard_normal;
static PyObject *__pyx_kp_u_standard_normal_size_None_dtype;
static PyObject *__pyx_kp_u_standard_t_df_size_None_Draw_sa;
static PyObject *__pyx_n_s_start;
static PyObject *__pyx_n_s_state;
static PyObject *__pyx_n_s_step;
static PyObject *__pyx_n_s_stop;
static PyObject *__pyx_n_s_str;
static PyObject *__pyx_kp_s_strided_and_direct;
static PyObject *__pyx_kp_s_strided_and_direct_or_indirect;
static PyObject *__pyx_kp_s_strided_and_indirect;
static PyObject *__pyx_n_s_strides;
static PyObject *__pyx_kp_s_stringsource;
static PyObject *__pyx_n_s_struct;
static PyObject *__pyx_n_s_subtract;
static PyObject *__pyx_kp_u_sum_colors_must_not_exceed_the_m;
static PyObject *__pyx_kp_u_sum_pvals_1_1_0;
static PyObject *__pyx_n_s_svd;
static PyObject *__pyx_n_u_svd;
static PyObject *__pyx_n_s_swapaxes;
static PyObject *__pyx_n_s_take;
static PyObject *__pyx_n_s_test;
static PyObject *__pyx_n_s_tobytes;
static PyObject *__pyx_n_s_tol;
static PyObject *__pyx_kp_u_triangular_left_mode_right_size;
static PyObject *__pyx_kp_u_u4;
static PyObject *__pyx_n_s_uint16;
static PyObject *__pyx_n_s_uint32;
static PyObject *__pyx_n_s_uint64;
static PyObject *__pyx_n_s_uint8;
static PyObject *__pyx_kp_s_unable_to_allocate_array_data;
static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;
static PyObject *__pyx_kp_u_uniform_low_0_0_high_1_0_size_N;
static PyObject *__pyx_n_s_unique;
static PyObject *__pyx_n_s_unpack;
static PyObject *__pyx_n_s_update;
static PyObject *__pyx_kp_u_vonmises_mu_kappa_size_None_Dra;
static PyObject *__pyx_kp_u_wald_mean_scale_size_None_Draw;
static PyObject *__pyx_n_s_warn;
static PyObject *__pyx_n_u_warn;
static PyObject *__pyx_n_s_warnings;
static PyObject *__pyx_kp_u_weibull_a_size_None_Draw_sample;
static PyObject *__pyx_n_s_x;
static PyObject *__pyx_n_s_zeros;
static PyObject *__pyx_n_u_zig;
static PyObject *__pyx_kp_u_zipf_a_size_None_Draw_samples_f;
static int __pyx_pf_5numpy_6random_10_generator_9Generator___init__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_bit_generator); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_2__repr__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_4__str__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_6__getstate__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_8__setstate__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_10__reduce__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_13bit_generator___get__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_12random(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype, PyObject *__pyx_v_out); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_14beta(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_16exponential(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_18standard_exponential(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype, PyObject *__pyx_v_method, PyObject *__pyx_v_out); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_20integers(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype, PyObject *__pyx_v_endpoint); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_22bytes(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, npy_intp __pyx_v_length); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_24choice(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size, PyObject *__pyx_v_replace, PyObject *__pyx_v_p, PyObject *__pyx_v_axis, int __pyx_v_shuffle); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_26uniform(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_28standard_normal(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype, PyObject *__pyx_v_out); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_30normal(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_32standard_gamma(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype, PyObject *__pyx_v_out); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_34gamma(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_36f(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_38noncentral_f(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_40chisquare(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_42noncentral_chisquare(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_44standard_cauchy(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_46standard_t(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_48vonmises(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_mu, PyObject *__pyx_v_kappa, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_50pareto(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_52weibull(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_54power(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_56laplace(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_58gumbel(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_60logistic(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_62lognormal(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_sigma, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_64rayleigh(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_66wald(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_68triangular(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_left, PyObject *__pyx_v_mode, PyObject *__pyx_v_right, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_70binomial(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_72negative_binomial(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_74poisson(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_lam, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_76zipf(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_78geometric(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_80hypergeometric(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_ngood, PyObject *__pyx_v_nbad, PyObject *__pyx_v_nsample, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_82logseries(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_84multivariate_normal(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_cov, PyObject *__pyx_v_size, PyObject *__pyx_v_check_valid, PyObject *__pyx_v_tol, PyObject *__pyx_v_method); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_86multinomial(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_pvals, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_88multivariate_hypergeometric(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_colors, PyObject *__pyx_v_nsample, PyObject *__pyx_v_size, PyObject *__pyx_v_method); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_90dirichlet(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_alpha, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_92shuffle(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_x, PyObject *__pyx_v_axis); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_94permutation(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_x, PyObject *__pyx_v_axis); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_9Generator_14_bit_generator___get__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self); /* proto */
static int __pyx_pf_5numpy_6random_10_generator_9Generator_14_bit_generator_2__set__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static int __pyx_pf_5numpy_6random_10_generator_9Generator_14_bit_generator_4__del__(struct __pyx_obj_5numpy_6random_10_generator_Generator *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_5numpy_6random_10_generator_default_rng(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_seed); /* proto */
static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
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static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
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static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
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/* Late includes */

/* "numpy/random/_generator.pyx":33
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  /* "numpy/random/_generator.pyx":33
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    /* "numpy/random/_generator.pyx":54
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 *     cdef const char *name = "BitGenerator"
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static char __pyx_doc_5numpy_6random_10_generator_9Generator_20integers[] = "\n        integers(low, high=None, size=None, dtype=np.int64, endpoint=False)\n\n        Return random integers from `low` (inclusive) to `high` (exclusive), or\n        if endpoint=True, `low` (inclusive) to `high` (inclusive). Replaces\n        `RandomState.randint` (with endpoint=False) and\n        `RandomState.random_integers` (with endpoint=True)\n\n        Return random integers from the \"discrete uniform\" distribution of\n        the specified dtype. If `high` is None (the default), then results are\n        from 0 to `low`.\n\n        Parameters\n        ----------\n        low : int or array-like of ints\n            Lowest (signed) integers to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is 0 and this value is\n            used for `high`).\n        high : int or array-like of ints, optional\n            If provided, one above the largest (signed) integer to be drawn\n            from the distribution (see above for behavior if ``high=None``).\n            If array-like, must contain integer values\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\n        dtype : dtype, optional\n            Desired dtype of the result. Byteorder must be native.\n            The default value is np.int64.\n        endpoint : bool, optional\n            If true, sample from the interval [low, high] instead of the\n            default [low, high)\n            Defaults to False\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        Notes\n        -----\n        When using broadcasting with uint64 dtypes, the maximum value (2**64)\n        cannot be repre""sented as a standard integer type. The high array (or\n        low if high is None) must have object dtype, e.g., array([2**64]).\n\n        Examples\n        --------\n        >>> rng = np.random.default_rng()\n        >>> rng.integers(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])  # random\n        >>> rng.integers(1, size=10)\n        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n        >>> rng.integers(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])  # random\n\n        Generate a 1 x 3 array with 3 different upper bounds\n\n        >>> rng.integers(1, [3, 5, 10])\n        array([2, 2, 9])  # random\n\n        Generate a 1 by 3 array with 3 different lower bounds\n\n        >>> rng.integers([1, 5, 7], 10)\n        array([9, 8, 7])  # random\n\n        Generate a 2 by 4 array using broadcasting with dtype of uint8\n\n        >>> rng.integers([1, 3, 5, 7], [[10], [20]], dtype=np.uint8)\n        array([[ 8,  6,  9,  7],\n               [ 1, 16,  9, 12]], dtype=uint8)  # random\n\n        References\n        ----------\n        .. [1] Daniel Lemire., \"Fast Random Integer Generation in an Interval\",\n               ACM Transactions on Modeling and Computer Simulation 29 (1), 2019,\n               http://arxiv.org/abs/1805.10941.\n\n        ";
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static char __pyx_doc_5numpy_6random_10_generator_9Generator_86multinomial[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalization of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the\n        outcome was ``i``.\n\n        Parameters\n        ----------\n        n : int or array-like of ints\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            must sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> rng = np.random.default_rng()\n        >>> rng.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])  # random\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> rng.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])  # rando""m\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        Now, do one experiment throwing the dice 10 time, and 10 times again,\n        and another throwing the dice 20 times, and 20 times again:\n\n        >>> rng.multinomial([[10], [20]], [1/6.]*6, size=(2, 2))\n        array([[[2, 4, 0, 1, 2, 1],\n                [1, 3, 0, 3, 1, 2]],\n               [[1, 4, 4, 4, 4, 3],\n                [3, 3, 2, 5, 5, 2]]])  # random\n\n        The first array shows the outcomes of throwing the dice 10 times, and\n        the second shows the outcomes from throwing the dice 20 times.\n\n        A loaded die is more likely to land on number 6:\n\n        >>> rng.multinomial(100, [1/7.]*5 + [2/7.])\n        array([11, 16, 14, 17, 16, 26])  # random\n\n        The probability inputs should be normalized. As an implementation\n        detail, the value of the last entry is ignored and assumed to take\n        up any leftover probability mass, but this should not be relied on.\n        A biased coin which has twice as much weight on one side as on the\n        other should be sampled like so:\n\n        >>> rng.multinomial(100, [1.0 / 3, 2.0 / 3])  # RIGHT\n        array([38, 62])  # random\n\n        not like:\n\n        >>> rng.multinomial(100, [1.0, 2.0])  # WRONG\n        Traceback (most recent call last):\n        ValueError: pvals < 0, pvals > 1 or pvals contains NaNs\n\n        ";
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static char __pyx_doc_5numpy_6random_10_generator_9Generator_88multivariate_hypergeometric[] = "\n        multivariate_hypergeometric(colors, nsample, size=None,\n                                    method='marginals')\n\n        Generate variates from a multivariate hypergeometric distribution.\n\n        The multivariate hypergeometric distribution is a generalization\n        of the hypergeometric distribution.\n\n        Choose ``nsample`` items at random without replacement from a\n        collection with ``N`` distinct types.  ``N`` is the length of\n        ``colors``, and the values in ``colors`` are the number of occurrences\n        of that type in the collection.  The total number of items in the\n        collection is ``sum(colors)``.  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The values in ``colors`` must be nonnegative.\n            To avoid loss of precision in the algorithm, ``sum(colors)``\n            must be less than ``10**9`` when `method` is \"marginals\".\n        nsample : int\n            The number of items selected.  ``nsample`` must not be greater\n            than ``sum(colors)``.\n        size : int or tuple of ints, optional\n            The number of variates to generate, either an integer or a tuple\n            holding the shape of the array of variates.  If the given size is,\n            e.g., ``(k, m)``, then ``k * m`` variates are drawn, where one\n            variate is ""a vector of length ``len(colors)``, and the return value\n            has shape ``(k, m, len(colors))``.  If `size` is an integer, the\n            output has shape ``(size, len(colors))``.  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See the Notes\n            for a description of the methods.\n\n        Returns\n        -------\n        variates : ndarray\n            Array of variates drawn from the multivariate hypergeometric\n            distribution.\n\n        See Also\n        --------\n        hypergeometric : Draw samples from the (univariate) hypergeometric\n            distribution.\n\n        Notes\n        -----\n        The two methods do not return the same sequence of variates.\n\n        The \"count\" algorithm is roughly equivalent to the following numpy\n        code::\n\n            choices = np.repeat(np.arange(len(colors)), colors)\n            selection = np.random.choice(choices, nsample, replace=False)\n            variate = np.bincount(selection, minlength=len(colors))\n\n        The \"count\" algorithm uses a temporary array of integers with length\n        ``sum(colors)``.\n\n        The \"marginals\" algorithm generates a variate by using repeated\n        calls to the univariate hypergeometric sampler.  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 */
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 */
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/* "View.MemoryView":807
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static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {
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 */
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 */
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 * 
 */
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 */
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      /* "View.MemoryView":842
 * 
 *         if have_start:
 *             if start < 0:             # <<<<<<<<<<<<<<
 *                 start += shape
 *                 if start < 0:
 */
      __pyx_t_2 = ((__pyx_v_start < 0) != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":843
 *         if have_start:
 *             if start < 0:
 *                 start += shape             # <<<<<<<<<<<<<<
 *                 if start < 0:
 *                     start = 0
 */
        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);

        /* "View.MemoryView":844
 *             if start < 0:
 *                 start += shape
 *                 if start < 0:             # <<<<<<<<<<<<<<
 *                     start = 0
 *             elif start >= shape:
 */
        __pyx_t_2 = ((__pyx_v_start < 0) != 0);
        if (__pyx_t_2) {

          /* "View.MemoryView":845
 *                 start += shape
 *                 if start < 0:
 *                     start = 0             # <<<<<<<<<<<<<<
 *             elif start >= shape:
 *                 if negative_step:
 */
          __pyx_v_start = 0;

          /* "View.MemoryView":844
 *             if start < 0:
 *                 start += shape
 *                 if start < 0:             # <<<<<<<<<<<<<<
 *                     start = 0
 *             elif start >= shape:
 */
        }

        /* "View.MemoryView":842
 * 
 *         if have_start:
 *             if start < 0:             # <<<<<<<<<<<<<<
 *                 start += shape
 *                 if start < 0:
 */
        goto __pyx_L12;
      }

      /* "View.MemoryView":846
 *                 if start < 0:
 *                     start = 0
 *             elif start >= shape:             # <<<<<<<<<<<<<<
 *                 if negative_step:
 *                     start = shape - 1
 */
      __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":847
 *                     start = 0
 *             elif start >= shape:
 *                 if negative_step:             # <<<<<<<<<<<<<<
 *                     start = shape - 1
 *                 else:
 */
        __pyx_t_2 = (__pyx_v_negative_step != 0);
        if (__pyx_t_2) {

          /* "View.MemoryView":848
 *             elif start >= shape:
 *                 if negative_step:
 *                     start = shape - 1             # <<<<<<<<<<<<<<
 *                 else:
 *                     start = shape
 */
          __pyx_v_start = (__pyx_v_shape - 1);

          /* "View.MemoryView":847
 *                     start = 0
 *             elif start >= shape:
 *                 if negative_step:             # <<<<<<<<<<<<<<
 *                     start = shape - 1
 *                 else:
 */
          goto __pyx_L14;
        }

        /* "View.MemoryView":850
 *                     start = shape - 1
 *                 else:
 *                     start = shape             # <<<<<<<<<<<<<<
 *         else:
 *             if negative_step:
 */
        /*else*/ {
          __pyx_v_start = __pyx_v_shape;
        }
        __pyx_L14:;

        /* "View.MemoryView":846
 *                 if start < 0:
 *                     start = 0
 *             elif start >= shape:             # <<<<<<<<<<<<<<
 *                 if negative_step:
 *                     start = shape - 1
 */
      }
      __pyx_L12:;

      /* "View.MemoryView":841
 * 
 * 
 *         if have_start:             # <<<<<<<<<<<<<<
 *             if start < 0:
 *                 start += shape
 */
      goto __pyx_L11;
    }

    /* "View.MemoryView":852
 *                     start = shape
 *         else:
 *             if negative_step:             # <<<<<<<<<<<<<<
 *                 start = shape - 1
 *             else:
 */
    /*else*/ {
      __pyx_t_2 = (__pyx_v_negative_step != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":853
 *         else:
 *             if negative_step:
 *                 start = shape - 1             # <<<<<<<<<<<<<<
 *             else:
 *                 start = 0
 */
        __pyx_v_start = (__pyx_v_shape - 1);

        /* "View.MemoryView":852
 *                     start = shape
 *         else:
 *             if negative_step:             # <<<<<<<<<<<<<<
 *                 start = shape - 1
 *             else:
 */
        goto __pyx_L15;
      }

      /* "View.MemoryView":855
 *                 start = shape - 1
 *             else:
 *                 start = 0             # <<<<<<<<<<<<<<
 * 
 *         if have_stop:
 */
      /*else*/ {
        __pyx_v_start = 0;
      }
      __pyx_L15:;
    }
    __pyx_L11:;

    /* "View.MemoryView":857
 *                 start = 0
 * 
 *         if have_stop:             # <<<<<<<<<<<<<<
 *             if stop < 0:
 *                 stop += shape
 */
    __pyx_t_2 = (__pyx_v_have_stop != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":858
 * 
 *         if have_stop:
 *             if stop < 0:             # <<<<<<<<<<<<<<
 *                 stop += shape
 *                 if stop < 0:
 */
      __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":859
 *         if have_stop:
 *             if stop < 0:
 *                 stop += shape             # <<<<<<<<<<<<<<
 *                 if stop < 0:
 *                     stop = 0
 */
        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);

        /* "View.MemoryView":860
 *             if stop < 0:
 *                 stop += shape
 *                 if stop < 0:             # <<<<<<<<<<<<<<
 *                     stop = 0
 *             elif stop > shape:
 */
        __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
        if (__pyx_t_2) {

          /* "View.MemoryView":861
 *                 stop += shape
 *                 if stop < 0:
 *                     stop = 0             # <<<<<<<<<<<<<<
 *             elif stop > shape:
 *                 stop = shape
 */
          __pyx_v_stop = 0;

          /* "View.MemoryView":860
 *             if stop < 0:
 *                 stop += shape
 *                 if stop < 0:             # <<<<<<<<<<<<<<
 *                     stop = 0
 *             elif stop > shape:
 */
        }

        /* "View.MemoryView":858
 * 
 *         if have_stop:
 *             if stop < 0:             # <<<<<<<<<<<<<<
 *                 stop += shape
 *                 if stop < 0:
 */
        goto __pyx_L17;
      }

      /* "View.MemoryView":862
 *                 if stop < 0:
 *                     stop = 0
 *             elif stop > shape:             # <<<<<<<<<<<<<<
 *                 stop = shape
 *         else:
 */
      __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":863
 *                     stop = 0
 *             elif stop > shape:
 *                 stop = shape             # <<<<<<<<<<<<<<
 *         else:
 *             if negative_step:
 */
        __pyx_v_stop = __pyx_v_shape;

        /* "View.MemoryView":862
 *                 if stop < 0:
 *                     stop = 0
 *             elif stop > shape:             # <<<<<<<<<<<<<<
 *                 stop = shape
 *         else:
 */
      }
      __pyx_L17:;

      /* "View.MemoryView":857
 *                 start = 0
 * 
 *         if have_stop:             # <<<<<<<<<<<<<<
 *             if stop < 0:
 *                 stop += shape
 */
      goto __pyx_L16;
    }

    /* "View.MemoryView":865
 *                 stop = shape
 *         else:
 *             if negative_step:             # <<<<<<<<<<<<<<
 *                 stop = -1
 *             else:
 */
    /*else*/ {
      __pyx_t_2 = (__pyx_v_negative_step != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":866
 *         else:
 *             if negative_step:
 *                 stop = -1             # <<<<<<<<<<<<<<
 *             else:
 *                 stop = shape
 */
        __pyx_v_stop = -1L;

        /* "View.MemoryView":865
 *                 stop = shape
 *         else:
 *             if negative_step:             # <<<<<<<<<<<<<<
 *                 stop = -1
 *             else:
 */
        goto __pyx_L19;
      }

      /* "View.MemoryView":868
 *                 stop = -1
 *             else:
 *                 stop = shape             # <<<<<<<<<<<<<<
 * 
 *         if not have_step:
 */
      /*else*/ {
        __pyx_v_stop = __pyx_v_shape;
      }
      __pyx_L19:;
    }
    __pyx_L16:;

    /* "View.MemoryView":870
 *                 stop = shape
 * 
 *         if not have_step:             # <<<<<<<<<<<<<<
 *             step = 1
 * 
 */
    __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":871
 * 
 *         if not have_step:
 *             step = 1             # <<<<<<<<<<<<<<
 * 
 * 
 */
      __pyx_v_step = 1;

      /* "View.MemoryView":870
 *                 stop = shape
 * 
 *         if not have_step:             # <<<<<<<<<<<<<<
 *             step = 1
 * 
 */
    }

    /* "View.MemoryView":875
 * 
 *         with cython.cdivision(True):
 *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<
 * 
 *             if (stop - start) - step * new_shape:
 */
    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);

    /* "View.MemoryView":877
 *             new_shape = (stop - start) // step
 * 
 *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
 *                 new_shape += 1
 * 
 */
    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":878
 * 
 *             if (stop - start) - step * new_shape:
 *                 new_shape += 1             # <<<<<<<<<<<<<<
 * 
 *         if new_shape < 0:
 */
      __pyx_v_new_shape = (__pyx_v_new_shape + 1);

      /* "View.MemoryView":877
 *             new_shape = (stop - start) // step
 * 
 *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
 *                 new_shape += 1
 * 
 */
    }

    /* "View.MemoryView":880
 *                 new_shape += 1
 * 
 *         if new_shape < 0:             # <<<<<<<<<<<<<<
 *             new_shape = 0
 * 
 */
    __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":881
 * 
 *         if new_shape < 0:
 *             new_shape = 0             # <<<<<<<<<<<<<<
 * 
 * 
 */
      __pyx_v_new_shape = 0;

      /* "View.MemoryView":880
 *                 new_shape += 1
 * 
 *         if new_shape < 0:             # <<<<<<<<<<<<<<
 *             new_shape = 0
 * 
 */
    }

    /* "View.MemoryView":884
 * 
 * 
 *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<
 *         dst.shape[new_ndim] = new_shape
 *         dst.suboffsets[new_ndim] = suboffset
 */
    (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);

    /* "View.MemoryView":885
 * 
 *         dst.strides[new_ndim] = stride * step
 *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<
 *         dst.suboffsets[new_ndim] = suboffset
 * 
 */
    (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;

    /* "View.MemoryView":886
 *         dst.strides[new_ndim] = stride * step
 *         dst.shape[new_ndim] = new_shape
 *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<
 * 
 * 
 */
    (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset;
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  __pyx_L3:;

  /* "View.MemoryView":889
 * 
 * 
 *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
 *         dst.data += start * stride
 *     else:
 */
  __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":890
 * 
 *     if suboffset_dim[0] < 0:
 *         dst.data += start * stride             # <<<<<<<<<<<<<<
 *     else:
 *         dst.suboffsets[suboffset_dim[0]] += start * stride
 */
    __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));

    /* "View.MemoryView":889
 * 
 * 
 *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
 *         dst.data += start * stride
 *     else:
 */
    goto __pyx_L23;
  }

  /* "View.MemoryView":892
 *         dst.data += start * stride
 *     else:
 *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<
 * 
 *     if suboffset >= 0:
 */
  /*else*/ {
    __pyx_t_3 = (__pyx_v_suboffset_dim[0]);
    (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride));
  }
  __pyx_L23:;

  /* "View.MemoryView":894
 *         dst.suboffsets[suboffset_dim[0]] += start * stride
 * 
 *     if suboffset >= 0:             # <<<<<<<<<<<<<<
 *         if not is_slice:
 *             if new_ndim == 0:
 */
  __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":895
 * 
 *     if suboffset >= 0:
 *         if not is_slice:             # <<<<<<<<<<<<<<
 *             if new_ndim == 0:
 *                 dst.data = (<char **> dst.data)[0] + suboffset
 */
    __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":896
 *     if suboffset >= 0:
 *         if not is_slice:
 *             if new_ndim == 0:             # <<<<<<<<<<<<<<
 *                 dst.data = (<char **> dst.data)[0] + suboffset
 *             else:
 */
      __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":897
 *         if not is_slice:
 *             if new_ndim == 0:
 *                 dst.data = (<char **> dst.data)[0] + suboffset             # <<<<<<<<<<<<<<
 *             else:
 *                 _err_dim(IndexError, "All dimensions preceding dimension %d "
 */
        __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset);

        /* "View.MemoryView":896
 *     if suboffset >= 0:
 *         if not is_slice:
 *             if new_ndim == 0:             # <<<<<<<<<<<<<<
 *                 dst.data = (<char **> dst.data)[0] + suboffset
 *             else:
 */
        goto __pyx_L26;
      }

      /* "View.MemoryView":899
 *                 dst.data = (<char **> dst.data)[0] + suboffset
 *             else:
 *                 _err_dim(IndexError, "All dimensions preceding dimension %d "             # <<<<<<<<<<<<<<
 *                                      "must be indexed and not sliced", dim)
 *         else:
 */
      /*else*/ {

        /* "View.MemoryView":900
 *             else:
 *                 _err_dim(IndexError, "All dimensions preceding dimension %d "
 *                                      "must be indexed and not sliced", dim)             # <<<<<<<<<<<<<<
 *         else:
 *             suboffset_dim[0] = new_ndim
 */
        __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 899, __pyx_L1_error)
      }
      __pyx_L26:;

      /* "View.MemoryView":895
 * 
 *     if suboffset >= 0:
 *         if not is_slice:             # <<<<<<<<<<<<<<
 *             if new_ndim == 0:
 *                 dst.data = (<char **> dst.data)[0] + suboffset
 */
      goto __pyx_L25;
    }

    /* "View.MemoryView":902
 *                                      "must be indexed and not sliced", dim)
 *         else:
 *             suboffset_dim[0] = new_ndim             # <<<<<<<<<<<<<<
 * 
 *     return 0
 */
    /*else*/ {
      (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim;
    }
    __pyx_L25:;

    /* "View.MemoryView":894
 *         dst.suboffsets[suboffset_dim[0]] += start * stride
 * 
 *     if suboffset >= 0:             # <<<<<<<<<<<<<<
 *         if not is_slice:
 *             if new_ndim == 0:
 */
  }

  /* "View.MemoryView":904
 *             suboffset_dim[0] = new_ndim
 * 
 *     return 0             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_r = 0;
  goto __pyx_L0;

  /* "View.MemoryView":807
 * 
 * @cname('__pyx_memoryview_slice_memviewslice')
 * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
 *         __Pyx_memviewslice *dst,
 *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
 */

  /* function exit code */
  __pyx_L1_error:;
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    #ifdef WITH_THREAD
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  __pyx_r = -1;
  __pyx_L0:;
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}

/* "View.MemoryView":910
 * 
 * @cname('__pyx_pybuffer_index')
 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<
 *                           Py_ssize_t dim) except NULL:
 *     cdef Py_ssize_t shape, stride, suboffset = -1
 */

static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) {
  Py_ssize_t __pyx_v_shape;
  Py_ssize_t __pyx_v_stride;
  Py_ssize_t __pyx_v_suboffset;
  Py_ssize_t __pyx_v_itemsize;
  char *__pyx_v_resultp;
  char *__pyx_r;
  __Pyx_RefNannyDeclarations
  Py_ssize_t __pyx_t_1;
  int __pyx_t_2;
  PyObject *__pyx_t_3 = NULL;
  PyObject *__pyx_t_4 = NULL;
  int __pyx_lineno = 0;
  const char *__pyx_filename = NULL;
  int __pyx_clineno = 0;
  __Pyx_RefNannySetupContext("pybuffer_index", 0);

  /* "View.MemoryView":912
 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,
 *                           Py_ssize_t dim) except NULL:
 *     cdef Py_ssize_t shape, stride, suboffset = -1             # <<<<<<<<<<<<<<
 *     cdef Py_ssize_t itemsize = view.itemsize
 *     cdef char *resultp
 */
  __pyx_v_suboffset = -1L;

  /* "View.MemoryView":913
 *                           Py_ssize_t dim) except NULL:
 *     cdef Py_ssize_t shape, stride, suboffset = -1
 *     cdef Py_ssize_t itemsize = view.itemsize             # <<<<<<<<<<<<<<
 *     cdef char *resultp
 * 
 */
  __pyx_t_1 = __pyx_v_view->itemsize;
  __pyx_v_itemsize = __pyx_t_1;

  /* "View.MemoryView":916
 *     cdef char *resultp
 * 
 *     if view.ndim == 0:             # <<<<<<<<<<<<<<
 *         shape = view.len / itemsize
 *         stride = itemsize
 */
  __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":917
 * 
 *     if view.ndim == 0:
 *         shape = view.len / itemsize             # <<<<<<<<<<<<<<
 *         stride = itemsize
 *     else:
 */
    if (unlikely(__pyx_v_itemsize == 0)) {
      PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero");
      __PYX_ERR(2, 917, __pyx_L1_error)
    }
    else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1)  && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) {
      PyErr_SetString(PyExc_OverflowError, "value too large to perform division");
      __PYX_ERR(2, 917, __pyx_L1_error)
    }
    __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize);

    /* "View.MemoryView":918
 *     if view.ndim == 0:
 *         shape = view.len / itemsize
 *         stride = itemsize             # <<<<<<<<<<<<<<
 *     else:
 *         shape = view.shape[dim]
 */
    __pyx_v_stride = __pyx_v_itemsize;

    /* "View.MemoryView":916
 *     cdef char *resultp
 * 
 *     if view.ndim == 0:             # <<<<<<<<<<<<<<
 *         shape = view.len / itemsize
 *         stride = itemsize
 */
    goto __pyx_L3;
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  /* "View.MemoryView":920
 *         stride = itemsize
 *     else:
 *         shape = view.shape[dim]             # <<<<<<<<<<<<<<
 *         stride = view.strides[dim]
 *         if view.suboffsets != NULL:
 */
  /*else*/ {
    __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]);

    /* "View.MemoryView":921
 *     else:
 *         shape = view.shape[dim]
 *         stride = view.strides[dim]             # <<<<<<<<<<<<<<
 *         if view.suboffsets != NULL:
 *             suboffset = view.suboffsets[dim]
 */
    __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]);

    /* "View.MemoryView":922
 *         shape = view.shape[dim]
 *         stride = view.strides[dim]
 *         if view.suboffsets != NULL:             # <<<<<<<<<<<<<<
 *             suboffset = view.suboffsets[dim]
 * 
 */
    __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":923
 *         stride = view.strides[dim]
 *         if view.suboffsets != NULL:
 *             suboffset = view.suboffsets[dim]             # <<<<<<<<<<<<<<
 * 
 *     if index < 0:
 */
      __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]);

      /* "View.MemoryView":922
 *         shape = view.shape[dim]
 *         stride = view.strides[dim]
 *         if view.suboffsets != NULL:             # <<<<<<<<<<<<<<
 *             suboffset = view.suboffsets[dim]
 * 
 */
    }
  }
  __pyx_L3:;

  /* "View.MemoryView":925
 *             suboffset = view.suboffsets[dim]
 * 
 *     if index < 0:             # <<<<<<<<<<<<<<
 *         index += view.shape[dim]
 *         if index < 0:
 */
  __pyx_t_2 = ((__pyx_v_index < 0) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":926
 * 
 *     if index < 0:
 *         index += view.shape[dim]             # <<<<<<<<<<<<<<
 *         if index < 0:
 *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
 */
    __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim]));

    /* "View.MemoryView":927
 *     if index < 0:
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 * 
 *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
 *         return 'C'
 *     else:
 */
  __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1135
 * 
 *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
 *         return 'C'             # <<<<<<<<<<<<<<
 *     else:
 *         return 'F'
 */
    __pyx_r = 'C';
    goto __pyx_L0;

    /* "View.MemoryView":1134
 *             break
 * 
 *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
 *         return 'C'
 *     else:
 */
  }

  /* "View.MemoryView":1137
 *         return 'C'
 *     else:
 *         return 'F'             # <<<<<<<<<<<<<<
 * 
 * @cython.cdivision(True)
 */
  /*else*/ {
    __pyx_r = 'F';
    goto __pyx_L0;
  }

  /* "View.MemoryView":1116
 * 
 * @cname('__pyx_get_best_slice_order')
 * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
 *     """
 *     Figure out the best memory access order for a given slice.
 */

  /* function exit code */
  __pyx_L0:;
  return __pyx_r;
}

/* "View.MemoryView":1140
 * 
 * @cython.cdivision(True)
 * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
 *                                    char *dst_data, Py_ssize_t *dst_strides,
 *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
 */

static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
  Py_ssize_t __pyx_v_dst_extent;
  Py_ssize_t __pyx_v_src_stride;
  Py_ssize_t __pyx_v_dst_stride;
  int __pyx_t_1;
  int __pyx_t_2;
  int __pyx_t_3;
  Py_ssize_t __pyx_t_4;
  Py_ssize_t __pyx_t_5;
  Py_ssize_t __pyx_t_6;

  /* "View.MemoryView":1147
 * 
 *     cdef Py_ssize_t i
 *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
 *     cdef Py_ssize_t dst_extent = dst_shape[0]
 *     cdef Py_ssize_t src_stride = src_strides[0]
 */
  __pyx_v_src_extent = (__pyx_v_src_shape[0]);

  /* "View.MemoryView":1148
 *     cdef Py_ssize_t i
 *     cdef Py_ssize_t src_extent = src_shape[0]
 *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
 *     cdef Py_ssize_t src_stride = src_strides[0]
 *     cdef Py_ssize_t dst_stride = dst_strides[0]
 */
  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);

  /* "View.MemoryView":1149
 *     cdef Py_ssize_t src_extent = src_shape[0]
 *     cdef Py_ssize_t dst_extent = dst_shape[0]
 *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
 *     cdef Py_ssize_t dst_stride = dst_strides[0]
 * 
 */
  __pyx_v_src_stride = (__pyx_v_src_strides[0]);

  /* "View.MemoryView":1150
 *     cdef Py_ssize_t dst_extent = dst_shape[0]
 *     cdef Py_ssize_t src_stride = src_strides[0]
 *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
 * 
 *     if ndim == 1:
 */
  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);

  /* "View.MemoryView":1152
 *     cdef Py_ssize_t dst_stride = dst_strides[0]
 * 
 *     if ndim == 1:             # <<<<<<<<<<<<<<
 *        if (src_stride > 0 and dst_stride > 0 and
 *            <size_t> src_stride == itemsize == <size_t> dst_stride):
 */
  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
  if (__pyx_t_1) {

    /* "View.MemoryView":1153
 * 
 *     if ndim == 1:
 *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
 *            <size_t> src_stride == itemsize == <size_t> dst_stride):
 *            memcpy(dst_data, src_data, itemsize * dst_extent)
 */
    __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0);
    if (__pyx_t_2) {
    } else {
      __pyx_t_1 = __pyx_t_2;
      goto __pyx_L5_bool_binop_done;
    }
    __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);
    if (__pyx_t_2) {
    } else {
      __pyx_t_1 = __pyx_t_2;
      goto __pyx_L5_bool_binop_done;
    }

    /* "View.MemoryView":1154
 *     if ndim == 1:
 *        if (src_stride > 0 and dst_stride > 0 and
 *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
 *            memcpy(dst_data, src_data, itemsize * dst_extent)
 *        else:
 */
    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
    if (__pyx_t_2) {
      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
    }
    __pyx_t_3 = (__pyx_t_2 != 0);
    __pyx_t_1 = __pyx_t_3;
    __pyx_L5_bool_binop_done:;

    /* "View.MemoryView":1153
 * 
 *     if ndim == 1:
 *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
 *            <size_t> src_stride == itemsize == <size_t> dst_stride):
 *            memcpy(dst_data, src_data, itemsize * dst_extent)
 */
    if (__pyx_t_1) {

      /* "View.MemoryView":1155
 *        if (src_stride > 0 and dst_stride > 0 and
 *            <size_t> src_stride == itemsize == <size_t> dst_stride):
 *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
 *        else:
 *            for i in range(dst_extent):
 */
      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));

      /* "View.MemoryView":1153
 * 
 *     if ndim == 1:
 *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
 *            <size_t> src_stride == itemsize == <size_t> dst_stride):
 *            memcpy(dst_data, src_data, itemsize * dst_extent)
 */
      goto __pyx_L4;
    }

    /* "View.MemoryView":1157
 *            memcpy(dst_data, src_data, itemsize * dst_extent)
 *        else:
 *            for i in range(dst_extent):             # <<<<<<<<<<<<<<
 *                memcpy(dst_data, src_data, itemsize)
 *                src_data += src_stride
 */
    /*else*/ {
      __pyx_t_4 = __pyx_v_dst_extent;
      __pyx_t_5 = __pyx_t_4;
      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
        __pyx_v_i = __pyx_t_6;

        /* "View.MemoryView":1158
 *        else:
 *            for i in range(dst_extent):
 *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
 *                src_data += src_stride
 *                dst_data += dst_stride
 */
        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));

        /* "View.MemoryView":1159
 *            for i in range(dst_extent):
 *                memcpy(dst_data, src_data, itemsize)
 *                src_data += src_stride             # <<<<<<<<<<<<<<
 *                dst_data += dst_stride
 *     else:
 */
        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);

        /* "View.MemoryView":1160
 *                memcpy(dst_data, src_data, itemsize)
 *                src_data += src_stride
 *                dst_data += dst_stride             # <<<<<<<<<<<<<<
 *     else:
 *         for i in range(dst_extent):
 */
        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
      }
    }
    __pyx_L4:;

    /* "View.MemoryView":1152
 *     cdef Py_ssize_t dst_stride = dst_strides[0]
 * 
 *     if ndim == 1:             # <<<<<<<<<<<<<<
 *        if (src_stride > 0 and dst_stride > 0 and
 *            <size_t> src_stride == itemsize == <size_t> dst_stride):
 */
    goto __pyx_L3;
  }

  /* "View.MemoryView":1162
 *                dst_data += dst_stride
 *     else:
 *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
 *             _copy_strided_to_strided(src_data, src_strides + 1,
 *                                      dst_data, dst_strides + 1,
 */
  /*else*/ {
    __pyx_t_4 = __pyx_v_dst_extent;
    __pyx_t_5 = __pyx_t_4;
    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
      __pyx_v_i = __pyx_t_6;

      /* "View.MemoryView":1163
 *     else:
 *         for i in range(dst_extent):
 *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
 *                                      dst_data, dst_strides + 1,
 *                                      src_shape + 1, dst_shape + 1,
 */
      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);

      /* "View.MemoryView":1167
 *                                      src_shape + 1, dst_shape + 1,
 *                                      ndim - 1, itemsize)
 *             src_data += src_stride             # <<<<<<<<<<<<<<
 *             dst_data += dst_stride
 * 
 */
      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);

      /* "View.MemoryView":1168
 *                                      ndim - 1, itemsize)
 *             src_data += src_stride
 *             dst_data += dst_stride             # <<<<<<<<<<<<<<
 * 
 * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
 */
      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
    }
  }
  __pyx_L3:;

  /* "View.MemoryView":1140
 * 
 * @cython.cdivision(True)
 * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
 *                                    char *dst_data, Py_ssize_t *dst_strides,
 *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
 */

  /* function exit code */
}

/* "View.MemoryView":1170
 *             dst_data += dst_stride
 * 
 * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
 *                                   __Pyx_memviewslice *dst,
 *                                   int ndim, size_t itemsize) nogil:
 */

static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {

  /* "View.MemoryView":1173
 *                                   __Pyx_memviewslice *dst,
 *                                   int ndim, size_t itemsize) nogil:
 *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
 *                              src.shape, dst.shape, ndim, itemsize)
 * 
 */
  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);

  /* "View.MemoryView":1170
 *             dst_data += dst_stride
 * 
 * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
 *                                   __Pyx_memviewslice *dst,
 *                                   int ndim, size_t itemsize) nogil:
 */

  /* function exit code */
}

/* "View.MemoryView":1177
 * 
 * @cname('__pyx_memoryview_slice_get_size')
 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
 *     "Return the size of the memory occupied by the slice in number of bytes"
 *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
 */

static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
  Py_ssize_t __pyx_v_shape;
  Py_ssize_t __pyx_v_size;
  Py_ssize_t __pyx_r;
  Py_ssize_t __pyx_t_1;
  Py_ssize_t *__pyx_t_2;
  Py_ssize_t *__pyx_t_3;
  Py_ssize_t *__pyx_t_4;

  /* "View.MemoryView":1179
 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:
 *     "Return the size of the memory occupied by the slice in number of bytes"
 *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
 * 
 *     for shape in src.shape[:ndim]:
 */
  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
  __pyx_v_size = __pyx_t_1;

  /* "View.MemoryView":1181
 *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
 * 
 *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<
 *         size *= shape
 * 
 */
  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
    __pyx_t_2 = __pyx_t_4;
    __pyx_v_shape = (__pyx_t_2[0]);

    /* "View.MemoryView":1182
 * 
 *     for shape in src.shape[:ndim]:
 *         size *= shape             # <<<<<<<<<<<<<<
 * 
 *     return size
 */
    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);
  }

  /* "View.MemoryView":1184
 *         size *= shape
 * 
 *     return size             # <<<<<<<<<<<<<<
 * 
 * @cname('__pyx_fill_contig_strides_array')
 */
  __pyx_r = __pyx_v_size;
  goto __pyx_L0;

  /* "View.MemoryView":1177
 * 
 * @cname('__pyx_memoryview_slice_get_size')
 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
 *     "Return the size of the memory occupied by the slice in number of bytes"
 *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
 */

  /* function exit code */
  __pyx_L0:;
  return __pyx_r;
}

/* "View.MemoryView":1187
 * 
 * @cname('__pyx_fill_contig_strides_array')
 * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
 *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
 *                 int ndim, char order) nogil:
 */

static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
  int __pyx_v_idx;
  Py_ssize_t __pyx_r;
  int __pyx_t_1;
  int __pyx_t_2;
  int __pyx_t_3;
  int __pyx_t_4;

  /* "View.MemoryView":1196
 *     cdef int idx
 * 
 *     if order == 'F':             # <<<<<<<<<<<<<<
 *         for idx in range(ndim):
 *             strides[idx] = stride
 */
  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
  if (__pyx_t_1) {

    /* "View.MemoryView":1197
 * 
 *     if order == 'F':
 *         for idx in range(ndim):             # <<<<<<<<<<<<<<
 *             strides[idx] = stride
 *             stride *= shape[idx]
 */
    __pyx_t_2 = __pyx_v_ndim;
    __pyx_t_3 = __pyx_t_2;
    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
      __pyx_v_idx = __pyx_t_4;

      /* "View.MemoryView":1198
 *     if order == 'F':
 *         for idx in range(ndim):
 *             strides[idx] = stride             # <<<<<<<<<<<<<<
 *             stride *= shape[idx]
 *     else:
 */
      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;

      /* "View.MemoryView":1199
 *         for idx in range(ndim):
 *             strides[idx] = stride
 *             stride *= shape[idx]             # <<<<<<<<<<<<<<
 *     else:
 *         for idx in range(ndim - 1, -1, -1):
 */
      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
    }

    /* "View.MemoryView":1196
 *     cdef int idx
 * 
 *     if order == 'F':             # <<<<<<<<<<<<<<
 *         for idx in range(ndim):
 *             strides[idx] = stride
 */
    goto __pyx_L3;
  }

  /* "View.MemoryView":1201
 *             stride *= shape[idx]
 *     else:
 *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
 *             strides[idx] = stride
 *             stride *= shape[idx]
 */
  /*else*/ {
    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
      __pyx_v_idx = __pyx_t_2;

      /* "View.MemoryView":1202
 *     else:
 *         for idx in range(ndim - 1, -1, -1):
 *             strides[idx] = stride             # <<<<<<<<<<<<<<
 *             stride *= shape[idx]
 * 
 */
      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;

      /* "View.MemoryView":1203
 *         for idx in range(ndim - 1, -1, -1):
 *             strides[idx] = stride
 *             stride *= shape[idx]             # <<<<<<<<<<<<<<
 * 
 *     return stride
 */
      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
    }
  }
  __pyx_L3:;

  /* "View.MemoryView":1205
 *             stride *= shape[idx]
 * 
 *     return stride             # <<<<<<<<<<<<<<
 * 
 * @cname('__pyx_memoryview_copy_data_to_temp')
 */
  __pyx_r = __pyx_v_stride;
  goto __pyx_L0;

  /* "View.MemoryView":1187
 * 
 * @cname('__pyx_fill_contig_strides_array')
 * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
 *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
 *                 int ndim, char order) nogil:
 */

  /* function exit code */
  __pyx_L0:;
  return __pyx_r;
}

/* "View.MemoryView":1208
 * 
 * @cname('__pyx_memoryview_copy_data_to_temp')
 * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
 *                              __Pyx_memviewslice *tmpslice,
 *                              char order,
 */

static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
  int __pyx_v_i;
  void *__pyx_v_result;
  size_t __pyx_v_itemsize;
  size_t __pyx_v_size;
  void *__pyx_r;
  Py_ssize_t __pyx_t_1;
  int __pyx_t_2;
  int __pyx_t_3;
  struct __pyx_memoryview_obj *__pyx_t_4;
  int __pyx_t_5;
  int __pyx_t_6;
  int __pyx_lineno = 0;
  const char *__pyx_filename = NULL;
  int __pyx_clineno = 0;

  /* "View.MemoryView":1219
 *     cdef void *result
 * 
 *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
 *     cdef size_t size = slice_get_size(src, ndim)
 * 
 */
  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
  __pyx_v_itemsize = __pyx_t_1;

  /* "View.MemoryView":1220
 * 
 *     cdef size_t itemsize = src.memview.view.itemsize
 *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<
 * 
 *     result = malloc(size)
 */
  __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);

  /* "View.MemoryView":1222
 *     cdef size_t size = slice_get_size(src, ndim)
 * 
 *     result = malloc(size)             # <<<<<<<<<<<<<<
 *     if not result:
 *         _err(MemoryError, NULL)
 */
  __pyx_v_result = malloc(__pyx_v_size);

  /* "View.MemoryView":1223
 * 
 *     result = malloc(size)
 *     if not result:             # <<<<<<<<<<<<<<
 *         _err(MemoryError, NULL)
 * 
 */
  __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1224
 *     result = malloc(size)
 *     if not result:
 *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<
 * 
 * 
 */
    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1224, __pyx_L1_error)

    /* "View.MemoryView":1223
 * 
 *     result = malloc(size)
 *     if not result:             # <<<<<<<<<<<<<<
 *         _err(MemoryError, NULL)
 * 
 */
  }

  /* "View.MemoryView":1227
 * 
 * 
 *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<
 *     tmpslice.memview = src.memview
 *     for i in range(ndim):
 */
  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);

  /* "View.MemoryView":1228
 * 
 *     tmpslice.data = <char *> result
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 *     cdef char order = get_best_order(&src, src_ndim)
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 *     cdef bint direct_copy = False             # <<<<<<<<<<<<<<
 *     cdef __Pyx_memviewslice tmp
 * 
 */
  __pyx_v_direct_copy = 0;

  /* "View.MemoryView":1284
 *     cdef __Pyx_memviewslice tmp
 * 
 *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
 *         broadcast_leading(&src, src_ndim, dst_ndim)
 *     elif dst_ndim < src_ndim:
 */
  __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1285
 * 
 *     if src_ndim < dst_ndim:
 *         broadcast_leading(&src, src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
 *     elif dst_ndim < src_ndim:
 *         broadcast_leading(&dst, dst_ndim, src_ndim)
 */
    __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim);

    /* "View.MemoryView":1284
 *     cdef __Pyx_memviewslice tmp
 * 
 *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
 *         broadcast_leading(&src, src_ndim, dst_ndim)
 *     elif dst_ndim < src_ndim:
 */
    goto __pyx_L3;
  }

  /* "View.MemoryView":1286
 *     if src_ndim < dst_ndim:
 *         broadcast_leading(&src, src_ndim, dst_ndim)
 *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
 *         broadcast_leading(&dst, dst_ndim, src_ndim)
 * 
 */
  __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1287
 *         broadcast_leading(&src, src_ndim, dst_ndim)
 *     elif dst_ndim < src_ndim:
 *         broadcast_leading(&dst, dst_ndim, src_ndim)             # <<<<<<<<<<<<<<
 * 
 *     cdef int ndim = max(src_ndim, dst_ndim)
 */
    __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim);

    /* "View.MemoryView":1286
 *     if src_ndim < dst_ndim:
 *         broadcast_leading(&src, src_ndim, dst_ndim)
 *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
 *         broadcast_leading(&dst, dst_ndim, src_ndim)
 * 
 */
  }
  __pyx_L3:;

  /* "View.MemoryView":1289
 *         broadcast_leading(&dst, dst_ndim, src_ndim)
 * 
 *     cdef int ndim = max(src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
 * 
 *     for i in range(ndim):
 */
  __pyx_t_3 = __pyx_v_dst_ndim;
  __pyx_t_4 = __pyx_v_src_ndim;
  if (((__pyx_t_3 > __pyx_t_4) != 0)) {
    __pyx_t_5 = __pyx_t_3;
  } else {
    __pyx_t_5 = __pyx_t_4;
  }
  __pyx_v_ndim = __pyx_t_5;

  /* "View.MemoryView":1291
 *     cdef int ndim = max(src_ndim, dst_ndim)
 * 
 *     for i in range(ndim):             # <<<<<<<<<<<<<<
 *         if src.shape[i] != dst.shape[i]:
 *             if src.shape[i] == 1:
 */
  __pyx_t_5 = __pyx_v_ndim;
  __pyx_t_3 = __pyx_t_5;
  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
    __pyx_v_i = __pyx_t_4;

    /* "View.MemoryView":1292
 * 
 *     for i in range(ndim):
 *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
 *             if src.shape[i] == 1:
 *                 broadcasting = True
 */
    __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":1293
 *     for i in range(ndim):
 *         if src.shape[i] != dst.shape[i]:
 *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
 *                 broadcasting = True
 *                 src.strides[i] = 0
 */
      __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":1294
 *         if src.shape[i] != dst.shape[i]:
 *             if src.shape[i] == 1:
 *                 broadcasting = True             # <<<<<<<<<<<<<<
 *                 src.strides[i] = 0
 *             else:
 */
        __pyx_v_broadcasting = 1;

        /* "View.MemoryView":1295
 *             if src.shape[i] == 1:
 *                 broadcasting = True
 *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
 *             else:
 *                 _err_extents(i, dst.shape[i], src.shape[i])
 */
        (__pyx_v_src.strides[__pyx_v_i]) = 0;

        /* "View.MemoryView":1293
 *     for i in range(ndim):
 *         if src.shape[i] != dst.shape[i]:
 *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
 *                 broadcasting = True
 *                 src.strides[i] = 0
 */
        goto __pyx_L7;
      }

      /* "View.MemoryView":1297
 *                 src.strides[i] = 0
 *             else:
 *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
 * 
 *         if src.suboffsets[i] >= 0:
 */
      /*else*/ {
        __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1297, __pyx_L1_error)
      }
      __pyx_L7:;

      /* "View.MemoryView":1292
 * 
 *     for i in range(ndim):
 *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
 *             if src.shape[i] == 1:
 *                 broadcasting = True
 */
    }

    /* "View.MemoryView":1299
 *                 _err_extents(i, dst.shape[i], src.shape[i])
 * 
 *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
 *             _err_dim(ValueError, "Dimension %d is not direct", i)
 * 
 */
    __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":1300
 * 
 *         if src.suboffsets[i] >= 0:
 *             _err_dim(ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
 * 
 *     if slices_overlap(&src, &dst, ndim, itemsize):
 */
      __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1300, __pyx_L1_error)

      /* "View.MemoryView":1299
 *                 _err_extents(i, dst.shape[i], src.shape[i])
 * 
 *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
 *             _err_dim(ValueError, "Dimension %d is not direct", i)
 * 
 */
    }
  }

  /* "View.MemoryView":1302
 *             _err_dim(ValueError, "Dimension %d is not direct", i)
 * 
 *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
 * 
 *         if not slice_is_contig(src, order, ndim):
 */
  __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1304
 *     if slices_overlap(&src, &dst, ndim, itemsize):
 * 
 *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
 *             order = get_best_order(&dst, ndim)
 * 
 */
    __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":1305
 * 
 *         if not slice_is_contig(src, order, ndim):
 *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
 * 
 *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
 */
      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);

      /* "View.MemoryView":1304
 *     if slices_overlap(&src, &dst, ndim, itemsize):
 * 
 *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
 *             order = get_best_order(&dst, ndim)
 * 
 */
    }

    /* "View.MemoryView":1307
 *             order = get_best_order(&dst, ndim)
 * 
 *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
 *         src = tmp
 * 
 */
    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1307, __pyx_L1_error)
    __pyx_v_tmpdata = __pyx_t_7;

    /* "View.MemoryView":1308
 * 
 *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
 *         src = tmp             # <<<<<<<<<<<<<<
 * 
 *     if not broadcasting:
 */
    __pyx_v_src = __pyx_v_tmp;

    /* "View.MemoryView":1302
 *             _err_dim(ValueError, "Dimension %d is not direct", i)
 * 
 *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
 * 
 *         if not slice_is_contig(src, order, ndim):
 */
  }

  /* "View.MemoryView":1310
 *         src = tmp
 * 
 *     if not broadcasting:             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1313
 * 
 * 
 *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
 *             direct_copy = slice_is_contig(dst, 'C', ndim)
 *         elif slice_is_contig(src, 'F', ndim):
 */
    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":1314
 * 
 *         if slice_is_contig(src, 'C', ndim):
 *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<
 *         elif slice_is_contig(src, 'F', ndim):
 *             direct_copy = slice_is_contig(dst, 'F', ndim)
 */
      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);

      /* "View.MemoryView":1313
 * 
 * 
 *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
 *             direct_copy = slice_is_contig(dst, 'C', ndim)
 *         elif slice_is_contig(src, 'F', ndim):
 */
      goto __pyx_L12;
    }

    /* "View.MemoryView":1315
 *         if slice_is_contig(src, 'C', ndim):
 *             direct_copy = slice_is_contig(dst, 'C', ndim)
 *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
 *             direct_copy = slice_is_contig(dst, 'F', ndim)
 * 
 */
    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":1316
 *             direct_copy = slice_is_contig(dst, 'C', ndim)
 *         elif slice_is_contig(src, 'F', ndim):
 *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<
 * 
 *         if direct_copy:
 */
      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);

      /* "View.MemoryView":1315
 *         if slice_is_contig(src, 'C', ndim):
 *             direct_copy = slice_is_contig(dst, 'C', ndim)
 *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
 *             direct_copy = slice_is_contig(dst, 'F', ndim)
 * 
 */
    }
    __pyx_L12:;

    /* "View.MemoryView":1318
 *             direct_copy = slice_is_contig(dst, 'F', ndim)
 * 
 *         if direct_copy:             # <<<<<<<<<<<<<<
 * 
 *             refcount_copying(&dst, dtype_is_object, ndim, False)
 */
    __pyx_t_2 = (__pyx_v_direct_copy != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":1320
 *         if direct_copy:
 * 
 *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
 *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
 *             refcount_copying(&dst, dtype_is_object, ndim, True)
 */
      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);

      /* "View.MemoryView":1321
 * 
 *             refcount_copying(&dst, dtype_is_object, ndim, False)
 *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
 *             refcount_copying(&dst, dtype_is_object, ndim, True)
 *             free(tmpdata)
 */
      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));

      /* "View.MemoryView":1322
 *             refcount_copying(&dst, dtype_is_object, ndim, False)
 *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
 *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
 *             free(tmpdata)
 *             return 0
 */
      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);

      /* "View.MemoryView":1323
 *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
 *             refcount_copying(&dst, dtype_is_object, ndim, True)
 *             free(tmpdata)             # <<<<<<<<<<<<<<
 *             return 0
 * 
 */
      free(__pyx_v_tmpdata);

      /* "View.MemoryView":1324
 *             refcount_copying(&dst, dtype_is_object, ndim, True)
 *             free(tmpdata)
 *             return 0             # <<<<<<<<<<<<<<
 * 
 *     if order == 'F' == get_best_order(&dst, ndim):
 */
      __pyx_r = 0;
      goto __pyx_L0;

      /* "View.MemoryView":1318
 *             direct_copy = slice_is_contig(dst, 'F', ndim)
 * 
 *         if direct_copy:             # <<<<<<<<<<<<<<
 * 
 *             refcount_copying(&dst, dtype_is_object, ndim, False)
 */
    }

    /* "View.MemoryView":1310
 *         src = tmp
 * 
 *     if not broadcasting:             # <<<<<<<<<<<<<<
 * 
 * 
 */
  }

  /* "View.MemoryView":1326
 *             return 0
 * 
 *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_t_2 = (__pyx_v_order == 'F');
  if (__pyx_t_2) {
    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
  }
  __pyx_t_8 = (__pyx_t_2 != 0);
  if (__pyx_t_8) {

    /* "View.MemoryView":1329
 * 
 * 
 *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
 *         transpose_memslice(&dst)
 * 
 */
    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1329, __pyx_L1_error)

    /* "View.MemoryView":1330
 * 
 *         transpose_memslice(&src)
 *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
 * 
 *     refcount_copying(&dst, dtype_is_object, ndim, False)
 */
    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1330, __pyx_L1_error)

    /* "View.MemoryView":1326
 *             return 0
 * 
 *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
 * 
 * 
 */
  }

  /* "View.MemoryView":1332
 *         transpose_memslice(&dst)
 * 
 *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
 *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
 *     refcount_copying(&dst, dtype_is_object, ndim, True)
 */
  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);

  /* "View.MemoryView":1333
 * 
 *     refcount_copying(&dst, dtype_is_object, ndim, False)
 *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
 *     refcount_copying(&dst, dtype_is_object, ndim, True)
 * 
 */
  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);

  /* "View.MemoryView":1334
 *     refcount_copying(&dst, dtype_is_object, ndim, False)
 *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
 *     refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
 * 
 *     free(tmpdata)
 */
  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);

  /* "View.MemoryView":1336
 *     refcount_copying(&dst, dtype_is_object, ndim, True)
 * 
 *     free(tmpdata)             # <<<<<<<<<<<<<<
 *     return 0
 * 
 */
  free(__pyx_v_tmpdata);

  /* "View.MemoryView":1337
 * 
 *     free(tmpdata)
 *     return 0             # <<<<<<<<<<<<<<
 * 
 * @cname('__pyx_memoryview_broadcast_leading')
 */
  __pyx_r = 0;
  goto __pyx_L0;

  /* "View.MemoryView":1268
 * 
 * @cname('__pyx_memoryview_copy_contents')
 * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
 *                                   __Pyx_memviewslice dst,
 *                                   int src_ndim, int dst_ndim,
 */

  /* function exit code */
  __pyx_L1_error:;
  {
    #ifdef WITH_THREAD
    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
    #endif
    __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
    #ifdef WITH_THREAD
    __Pyx_PyGILState_Release(__pyx_gilstate_save);
    #endif
  }
  __pyx_r = -1;
  __pyx_L0:;
  return __pyx_r;
}

/* "View.MemoryView":1340
 * 
 * @cname('__pyx_memoryview_broadcast_leading')
 * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
 *                             int ndim,
 *                             int ndim_other) nogil:
 */

static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) {
  int __pyx_v_i;
  int __pyx_v_offset;
  int __pyx_t_1;
  int __pyx_t_2;
  int __pyx_t_3;

  /* "View.MemoryView":1344
 *                             int ndim_other) nogil:
 *     cdef int i
 *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<
 * 
 *     for i in range(ndim - 1, -1, -1):
 */
  __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);

  /* "View.MemoryView":1346
 *     cdef int offset = ndim_other - ndim
 * 
 *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
 *         mslice.shape[i + offset] = mslice.shape[i]
 *         mslice.strides[i + offset] = mslice.strides[i]
 */
  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
    __pyx_v_i = __pyx_t_1;

    /* "View.MemoryView":1347
 * 
 *     for i in range(ndim - 1, -1, -1):
 *         mslice.shape[i + offset] = mslice.shape[i]             # <<<<<<<<<<<<<<
 *         mslice.strides[i + offset] = mslice.strides[i]
 *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
 */
    (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]);

    /* "View.MemoryView":1348
 *     for i in range(ndim - 1, -1, -1):
 *         mslice.shape[i + offset] = mslice.shape[i]
 *         mslice.strides[i + offset] = mslice.strides[i]             # <<<<<<<<<<<<<<
 *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
 * 
 */
    (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]);

    /* "View.MemoryView":1349
 *         mslice.shape[i + offset] = mslice.shape[i]
 *         mslice.strides[i + offset] = mslice.strides[i]
 *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]             # <<<<<<<<<<<<<<
 * 
 *     for i in range(offset):
 */
    (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]);
  }

  /* "View.MemoryView":1351
 *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
 * 
 *     for i in range(offset):             # <<<<<<<<<<<<<<
 *         mslice.shape[i] = 1
 *         mslice.strides[i] = mslice.strides[0]
 */
  __pyx_t_1 = __pyx_v_offset;
  __pyx_t_2 = __pyx_t_1;
  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
    __pyx_v_i = __pyx_t_3;

    /* "View.MemoryView":1352
 * 
 *     for i in range(offset):
 *         mslice.shape[i] = 1             # <<<<<<<<<<<<<<
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  {"uniform", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_27uniform, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_26uniform},
  {"standard_normal", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_29standard_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_28standard_normal},
  {"normal", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_31normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_30normal},
  {"standard_gamma", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_33standard_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_32standard_gamma},
  {"gamma", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_35gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_34gamma},
  {"f", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_37f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_36f},
  {"noncentral_f", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_39noncentral_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_38noncentral_f},
  {"chisquare", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_41chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_40chisquare},
  {"noncentral_chisquare", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_43noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_42noncentral_chisquare},
  {"standard_cauchy", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_45standard_cauchy, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_44standard_cauchy},
  {"standard_t", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_47standard_t, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_46standard_t},
  {"vonmises", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_49vonmises, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_48vonmises},
  {"pareto", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_51pareto, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_50pareto},
  {"weibull", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_53weibull, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_52weibull},
  {"power", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_55power, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_54power},
  {"laplace", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_57laplace, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_56laplace},
  {"gumbel", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_59gumbel, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_58gumbel},
  {"logistic", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_61logistic, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_60logistic},
  {"lognormal", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_63lognormal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_62lognormal},
  {"rayleigh", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_65rayleigh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_64rayleigh},
  {"wald", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_67wald, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_66wald},
  {"triangular", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_69triangular, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_68triangular},
  {"binomial", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_71binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_70binomial},
  {"negative_binomial", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_73negative_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_72negative_binomial},
  {"poisson", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_75poisson, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_74poisson},
  {"zipf", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_77zipf, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_76zipf},
  {"geometric", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_79geometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_78geometric},
  {"hypergeometric", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_81hypergeometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_80hypergeometric},
  {"logseries", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_83logseries, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_82logseries},
  {"multivariate_normal", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_85multivariate_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_84multivariate_normal},
  {"multinomial", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_87multinomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_86multinomial},
  {"multivariate_hypergeometric", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_89multivariate_hypergeometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_88multivariate_hypergeometric},
  {"dirichlet", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_91dirichlet, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_90dirichlet},
  {"shuffle", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_93shuffle, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_92shuffle},
  {"permutation", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_5numpy_6random_10_generator_9Generator_95permutation, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5numpy_6random_10_generator_9Generator_94permutation},
  {0, 0, 0, 0}
};

static struct PyGetSetDef __pyx_getsets_5numpy_6random_10_generator_Generator[] = {
  {(char *)"bit_generator", __pyx_getprop_5numpy_6random_10_generator_9Generator_bit_generator, 0, (char *)"\n        Gets the bit generator instance used by the generator\n\n        Returns\n        -------\n        bit_generator : BitGenerator\n            The bit generator instance used by the generator\n        ", 0},
  {(char *)"_bit_generator", __pyx_getprop_5numpy_6random_10_generator_9Generator__bit_generator, __pyx_setprop_5numpy_6random_10_generator_9Generator__bit_generator, (char *)0, 0},
  {0, 0, 0, 0, 0}
};

static PyTypeObject __pyx_type_5numpy_6random_10_generator_Generator = {
  PyVarObject_HEAD_INIT(0, 0)
  "numpy.random._generator.Generator", /*tp_name*/
  sizeof(struct __pyx_obj_5numpy_6random_10_generator_Generator), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_5numpy_6random_10_generator_Generator, /*tp_dealloc*/
  #if PY_VERSION_HEX < 0x030800b4
  0, /*tp_print*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4
  0, /*tp_vectorcall_offset*/
  #endif
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
  0, /*tp_as_async*/
  #endif
  __pyx_pw_5numpy_6random_10_generator_9Generator_3__repr__, /*tp_repr*/
  0, /*tp_as_number*/
  0, /*tp_as_sequence*/
  0, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  __pyx_pw_5numpy_6random_10_generator_9Generator_5__str__, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  0, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "\n    Generator(bit_generator)\n\n    Container for the BitGenerators.\n\n    ``Generator`` exposes a number of methods for generating random\n    numbers drawn from a variety of probability distributions. In addition to\n    the distribution-specific arguments, each method takes a keyword argument\n    `size` that defaults to ``None``. If `size` is ``None``, then a single\n    value is generated and returned. If `size` is an integer, then a 1-D\n    array filled with generated values is returned. If `size` is a tuple,\n    then an array with that shape is filled and returned.\n\n    The function :func:`numpy.random.default_rng` will instantiate\n    a `Generator` with numpy's default `BitGenerator`.\n\n    **No Compatibility Guarantee**\n\n    ``Generator`` does not provide a version compatibility guarantee. In\n    particular, as better algorithms evolve the bit stream may change.\n\n    Parameters\n    ----------\n    bit_generator : BitGenerator\n        BitGenerator to use as the core generator.\n\n    Notes\n    -----\n    The Python stdlib module `random` contains pseudo-random number generator\n    with a number of methods that are similar to the ones available in\n    ``Generator``. It uses Mersenne Twister, and this bit generator can\n    be accessed using ``MT19937``. ``Generator``, besides being\n    NumPy-aware, has the advantage that it provides a much larger number\n    of probability distributions to choose from.\n\n    Examples\n    --------\n    >>> from numpy.random import Generator, PCG64\n    >>> rg = Generator(PCG64())\n    >>> rg.standard_normal()\n    -0.203  # random\n\n    See Also\n    --------\n    default_rng : Recommended constructor for `Generator`.\n    ", /*tp_doc*/
  __pyx_tp_traverse_5numpy_6random_10_generator_Generator, /*tp_traverse*/
  __pyx_tp_clear_5numpy_6random_10_generator_Generator, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_5numpy_6random_10_generator_Generator, /*tp_methods*/
  0, /*tp_members*/
  __pyx_getsets_5numpy_6random_10_generator_Generator, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  __pyx_pw_5numpy_6random_10_generator_9Generator_1__init__, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_5numpy_6random_10_generator_Generator, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b1
  0, /*tp_vectorcall*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
  0, /*tp_print*/
  #endif
};
static struct __pyx_vtabstruct_array __pyx_vtable_array;

static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) {
  struct __pyx_array_obj *p;
  PyObject *o;
  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
    o = (*t->tp_alloc)(t, 0);
  } else {
    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
  }
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_array_obj *)o);
  p->__pyx_vtab = __pyx_vtabptr_array;
  p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None);
  p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None);
  if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) goto bad;
  return o;
  bad:
  Py_DECREF(o); o = 0;
  return NULL;
}

static void __pyx_tp_dealloc_array(PyObject *o) {
  struct __pyx_array_obj *p = (struct __pyx_array_obj *)o;
  #if CYTHON_USE_TP_FINALIZE
  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) {
    if (PyObject_CallFinalizerFromDealloc(o)) return;
  }
  #endif
  {
    PyObject *etype, *eval, *etb;
    PyErr_Fetch(&etype, &eval, &etb);
    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
    __pyx_array___dealloc__(o);
    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
    PyErr_Restore(etype, eval, etb);
  }
  Py_CLEAR(p->mode);
  Py_CLEAR(p->_format);
  (*Py_TYPE(o)->tp_free)(o);
}
static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) {
  PyObject *r;
  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
  Py_DECREF(x);
  return r;
}

static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) {
  if (v) {
    return __pyx_array___setitem__(o, i, v);
  }
  else {
    PyErr_Format(PyExc_NotImplementedError,
      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
    return -1;
  }
}

static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) {
  PyObject *v = __Pyx_PyObject_GenericGetAttr(o, n);
  if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) {
    PyErr_Clear();
    v = __pyx_array___getattr__(o, n);
  }
  return v;
}

static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o);
}

static PyMethodDef __pyx_methods_array[] = {
  {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0},
  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0},
  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0},
  {0, 0, 0, 0}
};

static struct PyGetSetDef __pyx_getsets_array[] = {
  {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0},
  {0, 0, 0, 0, 0}
};

static PySequenceMethods __pyx_tp_as_sequence_array = {
  __pyx_array___len__, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  __pyx_sq_item_array, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_array = {
  __pyx_array___len__, /*mp_length*/
  __pyx_array___getitem__, /*mp_subscript*/
  __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_array = {
  #if PY_MAJOR_VERSION < 3
  0, /*bf_getreadbuffer*/
  #endif
  #if PY_MAJOR_VERSION < 3
  0, /*bf_getwritebuffer*/
  #endif
  #if PY_MAJOR_VERSION < 3
  0, /*bf_getsegcount*/
  #endif
  #if PY_MAJOR_VERSION < 3
  0, /*bf_getcharbuffer*/
  #endif
  __pyx_array_getbuffer, /*bf_getbuffer*/
  0, /*bf_releasebuffer*/
};

static PyTypeObject __pyx_type___pyx_array = {
  PyVarObject_HEAD_INIT(0, 0)
  "numpy.random._generator.array", /*tp_name*/
  sizeof(struct __pyx_array_obj), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_array, /*tp_dealloc*/
  #if PY_VERSION_HEX < 0x030800b4
  0, /*tp_print*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4
  0, /*tp_vectorcall_offset*/
  #endif
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
  0, /*tp_as_async*/
  #endif
  0, /*tp_repr*/
  0, /*tp_as_number*/
  &__pyx_tp_as_sequence_array, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_array, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  0, /*tp_str*/
  __pyx_tp_getattro_array, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_array, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/
  0, /*tp_doc*/
  0, /*tp_traverse*/
  0, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_array, /*tp_methods*/
  0, /*tp_members*/
  __pyx_getsets_array, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_array, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b1
  0, /*tp_vectorcall*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
  0, /*tp_print*/
  #endif
};

static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
  struct __pyx_MemviewEnum_obj *p;
  PyObject *o;
  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
    o = (*t->tp_alloc)(t, 0);
  } else {
    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
  }
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_MemviewEnum_obj *)o);
  p->name = Py_None; Py_INCREF(Py_None);
  return o;
}

static void __pyx_tp_dealloc_Enum(PyObject *o) {
  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
  #if CYTHON_USE_TP_FINALIZE
  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
    if (PyObject_CallFinalizerFromDealloc(o)) return;
  }
  #endif
  PyObject_GC_UnTrack(o);
  Py_CLEAR(p->name);
  (*Py_TYPE(o)->tp_free)(o);
}

static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
  int e;
  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
  if (p->name) {
    e = (*v)(p->name, a); if (e) return e;
  }
  return 0;
}

static int __pyx_tp_clear_Enum(PyObject *o) {
  PyObject* tmp;
  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
  tmp = ((PyObject*)p->name);
  p->name = Py_None; Py_INCREF(Py_None);
  Py_XDECREF(tmp);
  return 0;
}

static PyMethodDef __pyx_methods_Enum[] = {
  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0},
  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0},
  {0, 0, 0, 0}
};

static PyTypeObject __pyx_type___pyx_MemviewEnum = {
  PyVarObject_HEAD_INIT(0, 0)
  "numpy.random._generator.Enum", /*tp_name*/
  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_Enum, /*tp_dealloc*/
  #if PY_VERSION_HEX < 0x030800b4
  0, /*tp_print*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4
  0, /*tp_vectorcall_offset*/
  #endif
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
  0, /*tp_as_async*/
  #endif
  __pyx_MemviewEnum___repr__, /*tp_repr*/
  0, /*tp_as_number*/
  0, /*tp_as_sequence*/
  0, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  0, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  0, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  0, /*tp_doc*/
  __pyx_tp_traverse_Enum, /*tp_traverse*/
  __pyx_tp_clear_Enum, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_Enum, /*tp_methods*/
  0, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  __pyx_MemviewEnum___init__, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_Enum, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b1
  0, /*tp_vectorcall*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
  0, /*tp_print*/
  #endif
};
static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;

static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
  struct __pyx_memoryview_obj *p;
  PyObject *o;
  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
    o = (*t->tp_alloc)(t, 0);
  } else {
    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
  }
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_memoryview_obj *)o);
  p->__pyx_vtab = __pyx_vtabptr_memoryview;
  p->obj = Py_None; Py_INCREF(Py_None);
  p->_size = Py_None; Py_INCREF(Py_None);
  p->_array_interface = Py_None; Py_INCREF(Py_None);
  p->view.obj = NULL;
  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;
  return o;
  bad:
  Py_DECREF(o); o = 0;
  return NULL;
}

static void __pyx_tp_dealloc_memoryview(PyObject *o) {
  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
  #if CYTHON_USE_TP_FINALIZE
  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
    if (PyObject_CallFinalizerFromDealloc(o)) return;
  }
  #endif
  PyObject_GC_UnTrack(o);
  {
    PyObject *etype, *eval, *etb;
    PyErr_Fetch(&etype, &eval, &etb);
    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
    __pyx_memoryview___dealloc__(o);
    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
    PyErr_Restore(etype, eval, etb);
  }
  Py_CLEAR(p->obj);
  Py_CLEAR(p->_size);
  Py_CLEAR(p->_array_interface);
  (*Py_TYPE(o)->tp_free)(o);
}

static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
  int e;
  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
  if (p->obj) {
    e = (*v)(p->obj, a); if (e) return e;
  }
  if (p->_size) {
    e = (*v)(p->_size, a); if (e) return e;
  }
  if (p->_array_interface) {
    e = (*v)(p->_array_interface, a); if (e) return e;
  }
  if (p->view.obj) {
    e = (*v)(p->view.obj, a); if (e) return e;
  }
  return 0;
}

static int __pyx_tp_clear_memoryview(PyObject *o) {
  PyObject* tmp;
  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
  tmp = ((PyObject*)p->obj);
  p->obj = Py_None; Py_INCREF(Py_None);
  Py_XDECREF(tmp);
  tmp = ((PyObject*)p->_size);
  p->_size = Py_None; Py_INCREF(Py_None);
  Py_XDECREF(tmp);
  tmp = ((PyObject*)p->_array_interface);
  p->_array_interface = Py_None; Py_INCREF(Py_None);
  Py_XDECREF(tmp);
  Py_CLEAR(p->view.obj);
  return 0;
}
static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
  PyObject *r;
  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
  Py_DECREF(x);
  return r;
}

static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
  if (v) {
    return __pyx_memoryview___setitem__(o, i, v);
  }
  else {
    PyErr_Format(PyExc_NotImplementedError,
      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
    return -1;
  }
}

static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
}

static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
}

static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
}

static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
}

static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
}

static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
}

static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
}

static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
}

static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
}

static PyMethodDef __pyx_methods_memoryview[] = {
  {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0},
  {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0},
  {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},
  {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},
  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},
  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},
  {0, 0, 0, 0}
};

static struct PyGetSetDef __pyx_getsets_memoryview[] = {
  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
  {0, 0, 0, 0, 0}
};

static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
  __pyx_memoryview___len__, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  __pyx_sq_item_memoryview, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
  __pyx_memoryview___len__, /*mp_length*/
  __pyx_memoryview___getitem__, /*mp_subscript*/
  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
  #if PY_MAJOR_VERSION < 3
  0, /*bf_getreadbuffer*/
  #endif
  #if PY_MAJOR_VERSION < 3
  0, /*bf_getwritebuffer*/
  #endif
  #if PY_MAJOR_VERSION < 3
  0, /*bf_getsegcount*/
  #endif
  #if PY_MAJOR_VERSION < 3
  0, /*bf_getcharbuffer*/
  #endif
  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
  0, /*bf_releasebuffer*/
};

static PyTypeObject __pyx_type___pyx_memoryview = {
  PyVarObject_HEAD_INIT(0, 0)
  "numpy.random._generator.memoryview", /*tp_name*/
  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
  #if PY_VERSION_HEX < 0x030800b4
  0, /*tp_print*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4
  0, /*tp_vectorcall_offset*/
  #endif
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
  0, /*tp_as_async*/
  #endif
  __pyx_memoryview___repr__, /*tp_repr*/
  0, /*tp_as_number*/
  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  __pyx_memoryview___str__, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  0, /*tp_doc*/
  __pyx_tp_traverse_memoryview, /*tp_traverse*/
  __pyx_tp_clear_memoryview, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_memoryview, /*tp_methods*/
  0, /*tp_members*/
  __pyx_getsets_memoryview, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_memoryview, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b1
  0, /*tp_vectorcall*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
  0, /*tp_print*/
  #endif
};
static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;

static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
  struct __pyx_memoryviewslice_obj *p;
  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_memoryviewslice_obj *)o);
  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
  p->from_object = Py_None; Py_INCREF(Py_None);
  p->from_slice.memview = NULL;
  return o;
}

static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
  #if CYTHON_USE_TP_FINALIZE
  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
    if (PyObject_CallFinalizerFromDealloc(o)) return;
  }
  #endif
  PyObject_GC_UnTrack(o);
  {
    PyObject *etype, *eval, *etb;
    PyErr_Fetch(&etype, &eval, &etb);
    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
    __pyx_memoryviewslice___dealloc__(o);
    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
    PyErr_Restore(etype, eval, etb);
  }
  Py_CLEAR(p->from_object);
  PyObject_GC_Track(o);
  __pyx_tp_dealloc_memoryview(o);
}

static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
  int e;
  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
  if (p->from_object) {
    e = (*v)(p->from_object, a); if (e) return e;
  }
  return 0;
}

static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
  PyObject* tmp;
  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
  __pyx_tp_clear_memoryview(o);
  tmp = ((PyObject*)p->from_object);
  p->from_object = Py_None; Py_INCREF(Py_None);
  Py_XDECREF(tmp);
  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);
  return 0;
}

static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {
  return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o);
}

static PyMethodDef __pyx_methods__memoryviewslice[] = {
  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},
  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},
  {0, 0, 0, 0}
};

static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {
  {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0},
  {0, 0, 0, 0, 0}
};

static PyTypeObject __pyx_type___pyx_memoryviewslice = {
  PyVarObject_HEAD_INIT(0, 0)
  "numpy.random._generator._memoryviewslice", /*tp_name*/
  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
  #if PY_VERSION_HEX < 0x030800b4
  0, /*tp_print*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4
  0, /*tp_vectorcall_offset*/
  #endif
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
  0, /*tp_as_async*/
  #endif
  #if CYTHON_COMPILING_IN_PYPY
  __pyx_memoryview___repr__, /*tp_repr*/
  #else
  0, /*tp_repr*/
  #endif
  0, /*tp_as_number*/
  0, /*tp_as_sequence*/
  0, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  #if CYTHON_COMPILING_IN_PYPY
  __pyx_memoryview___str__, /*tp_str*/
  #else
  0, /*tp_str*/
  #endif
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  0, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "Internal class for passing memoryview slices to Python", /*tp_doc*/
  __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/
  __pyx_tp_clear__memoryviewslice, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods__memoryviewslice, /*tp_methods*/
  0, /*tp_members*/
  __pyx_getsets__memoryviewslice, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new__memoryviewslice, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b1
  0, /*tp_vectorcall*/
  #endif
  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
  0, /*tp_print*/
  #endif
};

static PyMethodDef __pyx_methods[] = {
  {0, 0, 0, 0}
};

#if PY_MAJOR_VERSION >= 3
#if CYTHON_PEP489_MULTI_PHASE_INIT
static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/
static int __pyx_pymod_exec__generator(PyObject* module); /*proto*/
static PyModuleDef_Slot __pyx_moduledef_slots[] = {
  {Py_mod_create, (void*)__pyx_pymod_create},
  {Py_mod_exec, (void*)__pyx_pymod_exec__generator},
  {0, NULL}
};
#endif

static struct PyModuleDef __pyx_moduledef = {
    PyModuleDef_HEAD_INIT,
    "_generator",
    0, /* m_doc */
  #if CYTHON_PEP489_MULTI_PHASE_INIT
    0, /* m_size */
  #else
    -1, /* m_size */
  #endif
    __pyx_methods /* m_methods */,
  #if CYTHON_PEP489_MULTI_PHASE_INIT
    __pyx_moduledef_slots, /* m_slots */
  #else
    NULL, /* m_reload */
  #endif
    NULL, /* m_traverse */
    NULL, /* m_clear */
    NULL /* m_free */
};
#endif
#ifndef CYTHON_SMALL_CODE
#if defined(__clang__)
    #define CYTHON_SMALL_CODE
#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3))
    #define CYTHON_SMALL_CODE __attribute__((cold))
#else
    #define CYTHON_SMALL_CODE
#endif
#endif

static __Pyx_StringTabEntry __pyx_string_tab[] = {
  {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1},
  {&__pyx_kp_u_Axis_argument_is_only_supported, __pyx_k_Axis_argument_is_only_supported, sizeof(__pyx_k_Axis_argument_is_only_supported), 0, 1, 0, 0},
  {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0},
  {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0},
  {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0},
  {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0},
  {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0},
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  {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1},
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  {&__pyx_kp_u_Generator_shuffle_line_4114, __pyx_k_Generator_shuffle_line_4114, sizeof(__pyx_k_Generator_shuffle_line_4114), 0, 1, 0, 0},
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  /* "numpy/random/_generator.pyx":754
 *             # array, taking into account that np.array(item) may not work
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  /* "numpy/random/_generator.pyx":846
 *             range = _high - _low
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 *                 raise OverflowError('Range exceeds valid bounds')             # <<<<<<<<<<<<<<
 * 
 *             return cont(&random_uniform, &self._bitgen, size, self.lock, 2,
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  __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_u_Range_exceeds_valid_bounds); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(0, 846, __pyx_L1_error)
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  /* "numpy/random/_generator.pyx":2674
 * 
 *             if fleft > fmode:
 *                 raise ValueError("left > mode")             # <<<<<<<<<<<<<<
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  /* "numpy/random/_generator.pyx":2676
 *                 raise ValueError("left > mode")
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 *                 raise ValueError("mode > right")             # <<<<<<<<<<<<<<
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  /* "numpy/random/_generator.pyx":2678
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 *                 raise ValueError("left == right")             # <<<<<<<<<<<<<<
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  /* "numpy/random/_generator.pyx":3245
 *                                  HYPERGEOM_MAX)
 *             if lngood + lnbad < lnsample:
 *                 raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
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  /* "numpy/random/_generator.pyx":3462
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 *             raise ValueError(             # <<<<<<<<<<<<<<
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 */
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  /* "numpy/random/_generator.pyx":3476
 * 
 *         if len(mean.shape) != 1:
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  /* "numpy/random/_generator.pyx":3478
 *             raise ValueError("mean must be 1 dimensional")
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  __pyx_tuple__32 = PyTuple_Pack(1, __pyx_kp_u_cov_must_be_2_dimensional_and_sq); if (unlikely(!__pyx_tuple__32)) __PYX_ERR(0, 3478, __pyx_L1_error)
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 *             raise ValueError("cov must be 2 dimensional and square")
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 *         # Compute shape of output and create a matrix of independent
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 *                 raise ValueError(             # <<<<<<<<<<<<<<
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  /* "numpy/random/_generator.pyx":3529
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  /* "numpy/random/_generator.pyx":3532
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 *                 else:
 *                     raise ValueError("covariance is not positive-semidefinite.")             # <<<<<<<<<<<<<<
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 *         if method == 'cholesky':
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 *         check_array_constraint(parr, 'pvals', CONS_BOUNDED_0_1)
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 *         if np.PyArray_NDIM(on) != 0: # vector
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  /* "numpy/random/_generator.pyx":3825
 * 
 *         if method not in ['count', 'marginals']:
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 *         try:
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  /* "numpy/random/_generator.pyx":3830
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 *             raise ValueError('nsample must be an integer')             # <<<<<<<<<<<<<<
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 *         if nsample < 0:
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 * 
 *         if nsample < 0:
 *             raise ValueError("nsample must be nonnegative.")             # <<<<<<<<<<<<<<
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 * 
 *         if method == 'marginals' and total >= 1000000000:
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 */
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 */
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 * cdef inline int import_umath() except -1:
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 *         _import_umath()
 *     except Exception:
 *         raise ImportError("numpy.core.umath failed to import")             # <<<<<<<<<<<<<<
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 * cdef inline int import_ufunc() except -1:
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 *         if not self.ndim:
 *             raise ValueError("Empty shape tuple for cython.array")             # <<<<<<<<<<<<<<
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 *         if itemsize <= 0:
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 *         if not self._shape:
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 * def __reduce_cython__(self):
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  /* "View.MemoryView":316
 * 
 * DEF THREAD_LOCKS_PREALLOCATED = 8
 * cdef int __pyx_memoryview_thread_locks_used = 0             # <<<<<<<<<<<<<<
 * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [
 *     PyThread_allocate_lock(),
 */
  __pyx_memoryview_thread_locks_used = 0;

  /* "View.MemoryView":317
 * DEF THREAD_LOCKS_PREALLOCATED = 8
 * cdef int __pyx_memoryview_thread_locks_used = 0
 * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [             # <<<<<<<<<<<<<<
 *     PyThread_allocate_lock(),
 *     PyThread_allocate_lock(),
 */
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  __pyx_t_4[5] = PyThread_allocate_lock();
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  memcpy(&(__pyx_memoryview_thread_locks[0]), __pyx_t_4, sizeof(__pyx_memoryview_thread_locks[0]) * (8));

  /* "View.MemoryView":549
 *         info.obj = self
 * 
 *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")             # <<<<<<<<<<<<<<
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 * 
 */
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 *         return self.from_object
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 *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")             # <<<<<<<<<<<<<<
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 * 
 */
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 */
  __pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum, NULL, __pyx_n_s_View_MemoryView); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1, __pyx_L1_error)
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 */

  /*--- Wrapped vars code ---*/

  goto __pyx_L0;
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    Py_CLEAR(__pyx_m);
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  return (__pyx_m != NULL) ? 0 : -1;
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/* --- Runtime support code --- */
/* Refnanny */
#if CYTHON_REFNANNY
static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
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    p = PyObject_GetAttrString(m, "RefNannyAPI");
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end:
    Py_XDECREF(p);
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/* PyObjectGetAttrStr */
#if CYTHON_USE_TYPE_SLOTS
static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
    PyTypeObject* tp = Py_TYPE(obj);
    if (likely(tp->tp_getattro))
        return tp->tp_getattro(obj, attr_name);
#if PY_MAJOR_VERSION < 3
    if (likely(tp->tp_getattr))
        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
#endif
    return PyObject_GetAttr(obj, attr_name);
}
#endif

/* GetBuiltinName */
static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
    if (unlikely(!result)) {
        PyErr_Format(PyExc_NameError,
#if PY_MAJOR_VERSION >= 3
            "name '%U' is not defined", name);
#else
            "name '%.200s' is not defined", PyString_AS_STRING(name));
#endif
    }
    return result;
}

/* GetAttr */
static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
#if CYTHON_USE_TYPE_SLOTS
#if PY_MAJOR_VERSION >= 3
    if (likely(PyUnicode_Check(n)))
#else
    if (likely(PyString_Check(n)))
#endif
        return __Pyx_PyObject_GetAttrStr(o, n);
#endif
    return PyObject_GetAttr(o, n);
}

/* HasAttr */
static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
    PyObject *r;
    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
        PyErr_SetString(PyExc_TypeError,
                        "hasattr(): attribute name must be string");
        return -1;
    }
    r = __Pyx_GetAttr(o, n);
    if (unlikely(!r)) {
        PyErr_Clear();
        return 0;
    } else {
        Py_DECREF(r);
        return 1;
    }
}

/* PyErrFetchRestore */
#if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    tmp_type = tstate->curexc_type;
    tmp_value = tstate->curexc_value;
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    tstate->curexc_type = type;
    tstate->curexc_value = value;
    tstate->curexc_traceback = tb;
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
}
static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    *type = tstate->curexc_type;
    *value = tstate->curexc_value;
    *tb = tstate->curexc_traceback;
    tstate->curexc_type = 0;
    tstate->curexc_value = 0;
    tstate->curexc_traceback = 0;
}
#endif

/* WriteUnraisableException */
static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno,
                                  CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename,
                                  int full_traceback, CYTHON_UNUSED int nogil) {
    PyObject *old_exc, *old_val, *old_tb;
    PyObject *ctx;
    __Pyx_PyThreadState_declare
#ifdef WITH_THREAD
    PyGILState_STATE state;
    if (nogil)
        state = PyGILState_Ensure();
#ifdef _MSC_VER
    else state = (PyGILState_STATE)-1;
#endif
#endif
    __Pyx_PyThreadState_assign
    __Pyx_ErrFetch(&old_exc, &old_val, &old_tb);
    if (full_traceback) {
        Py_XINCREF(old_exc);
        Py_XINCREF(old_val);
        Py_XINCREF(old_tb);
        __Pyx_ErrRestore(old_exc, old_val, old_tb);
        PyErr_PrintEx(1);
    }
    #if PY_MAJOR_VERSION < 3
    ctx = PyString_FromString(name);
    #else
    ctx = PyUnicode_FromString(name);
    #endif
    __Pyx_ErrRestore(old_exc, old_val, old_tb);
    if (!ctx) {
        PyErr_WriteUnraisable(Py_None);
    } else {
        PyErr_WriteUnraisable(ctx);
        Py_DECREF(ctx);
    }
#ifdef WITH_THREAD
    if (nogil)
        PyGILState_Release(state);
#endif
}

/* RaiseDoubleKeywords */
static void __Pyx_RaiseDoubleKeywordsError(
    const char* func_name,
    PyObject* kw_name)
{
    PyErr_Format(PyExc_TypeError,
        #if PY_MAJOR_VERSION >= 3
        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
        #else
        "%s() got multiple values for keyword argument '%s'", func_name,
        PyString_AsString(kw_name));
        #endif
}

/* ParseKeywords */
static int __Pyx_ParseOptionalKeywords(
    PyObject *kwds,
    PyObject **argnames[],
    PyObject *kwds2,
    PyObject *values[],
    Py_ssize_t num_pos_args,
    const char* function_name)
{
    PyObject *key = 0, *value = 0;
    Py_ssize_t pos = 0;
    PyObject*** name;
    PyObject*** first_kw_arg = argnames + num_pos_args;
    while (PyDict_Next(kwds, &pos, &key, &value)) {
        name = first_kw_arg;
        while (*name && (**name != key)) name++;
        if (*name) {
            values[name-argnames] = value;
            continue;
        }
        name = first_kw_arg;
        #if PY_MAJOR_VERSION < 3
        if (likely(PyString_Check(key))) {
            while (*name) {
                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
                        && _PyString_Eq(**name, key)) {
                    values[name-argnames] = value;
                    break;
                }
                name++;
            }
            if (*name) continue;
            else {
                PyObject*** argname = argnames;
                while (argname != first_kw_arg) {
                    if ((**argname == key) || (
                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
                             && _PyString_Eq(**argname, key))) {
                        goto arg_passed_twice;
                    }
                    argname++;
                }
            }
        } else
        #endif
        if (likely(PyUnicode_Check(key))) {
            while (*name) {
                int cmp = (**name == key) ? 0 :
                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
                #endif
                    PyUnicode_Compare(**name, key);
                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
                if (cmp == 0) {
                    values[name-argnames] = value;
                    break;
                }
                name++;
            }
            if (*name) continue;
            else {
                PyObject*** argname = argnames;
                while (argname != first_kw_arg) {
                    int cmp = (**argname == key) ? 0 :
                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
                    #endif
                        PyUnicode_Compare(**argname, key);
                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
                    if (cmp == 0) goto arg_passed_twice;
                    argname++;
                }
            }
        } else
            goto invalid_keyword_type;
        if (kwds2) {
            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
        } else {
            goto invalid_keyword;
        }
    }
    return 0;
arg_passed_twice:
    __Pyx_RaiseDoubleKeywordsError(function_name, key);
    goto bad;
invalid_keyword_type:
    PyErr_Format(PyExc_TypeError,
        "%.200s() keywords must be strings", function_name);
    goto bad;
invalid_keyword:
    PyErr_Format(PyExc_TypeError,
    #if PY_MAJOR_VERSION < 3
        "%.200s() got an unexpected keyword argument '%.200s'",
        function_name, PyString_AsString(key));
    #else
        "%s() got an unexpected keyword argument '%U'",
        function_name, key);
    #endif
bad:
    return -1;
}

/* RaiseArgTupleInvalid */
static void __Pyx_RaiseArgtupleInvalid(
    const char* func_name,
    int exact,
    Py_ssize_t num_min,
    Py_ssize_t num_max,
    Py_ssize_t num_found)
{
    Py_ssize_t num_expected;
    const char *more_or_less;
    if (num_found < num_min) {
        num_expected = num_min;
        more_or_less = "at least";
    } else {
        num_expected = num_max;
        more_or_less = "at most";
    }
    if (exact) {
        more_or_less = "exactly";
    }
    PyErr_Format(PyExc_TypeError,
                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
                 func_name, more_or_less, num_expected,
                 (num_expected == 1) ? "" : "s", num_found);
}

/* PyObjectCall */
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
    PyObject *result;
    ternaryfunc call = func->ob_type->tp_call;
    if (unlikely(!call))
        return PyObject_Call(func, arg, kw);
    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
        return NULL;
    result = (*call)(func, arg, kw);
    Py_LeaveRecursiveCall();
    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
        PyErr_SetString(
            PyExc_SystemError,
            "NULL result without error in PyObject_Call");
    }
    return result;
}
#endif

/* RaiseException */
#if PY_MAJOR_VERSION < 3
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
                        CYTHON_UNUSED PyObject *cause) {
    __Pyx_PyThreadState_declare
    Py_XINCREF(type);
    if (!value || value == Py_None)
        value = NULL;
    else
        Py_INCREF(value);
    if (!tb || tb == Py_None)
        tb = NULL;
    else {
        Py_INCREF(tb);
        if (!PyTraceBack_Check(tb)) {
            PyErr_SetString(PyExc_TypeError,
                "raise: arg 3 must be a traceback or None");
            goto raise_error;
        }
    }
    if (PyType_Check(type)) {
#if CYTHON_COMPILING_IN_PYPY
        if (!value) {
            Py_INCREF(Py_None);
            value = Py_None;
        }
#endif
        PyErr_NormalizeException(&type, &value, &tb);
    } else {
        if (value) {
            PyErr_SetString(PyExc_TypeError,
                "instance exception may not have a separate value");
            goto raise_error;
        }
        value = type;
        type = (PyObject*) Py_TYPE(type);
        Py_INCREF(type);
        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
            PyErr_SetString(PyExc_TypeError,
                "raise: exception class must be a subclass of BaseException");
            goto raise_error;
        }
    }
    __Pyx_PyThreadState_assign
    __Pyx_ErrRestore(type, value, tb);
    return;
raise_error:
    Py_XDECREF(value);
    Py_XDECREF(type);
    Py_XDECREF(tb);
    return;
}
#else
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
    PyObject* owned_instance = NULL;
    if (tb == Py_None) {
        tb = 0;
    } else if (tb && !PyTraceBack_Check(tb)) {
        PyErr_SetString(PyExc_TypeError,
            "raise: arg 3 must be a traceback or None");
        goto bad;
    }
    if (value == Py_None)
        value = 0;
    if (PyExceptionInstance_Check(type)) {
        if (value) {
            PyErr_SetString(PyExc_TypeError,
                "instance exception may not have a separate value");
            goto bad;
        }
        value = type;
        type = (PyObject*) Py_TYPE(value);
    } else if (PyExceptionClass_Check(type)) {
        PyObject *instance_class = NULL;
        if (value && PyExceptionInstance_Check(value)) {
            instance_class = (PyObject*) Py_TYPE(value);
            if (instance_class != type) {
                int is_subclass = PyObject_IsSubclass(instance_class, type);
                if (!is_subclass) {
                    instance_class = NULL;
                } else if (unlikely(is_subclass == -1)) {
                    goto bad;
                } else {
                    type = instance_class;
                }
            }
        }
        if (!instance_class) {
            PyObject *args;
            if (!value)
                args = PyTuple_New(0);
            else if (PyTuple_Check(value)) {
                Py_INCREF(value);
                args = value;
            } else
                args = PyTuple_Pack(1, value);
            if (!args)
                goto bad;
            owned_instance = PyObject_Call(type, args, NULL);
            Py_DECREF(args);
            if (!owned_instance)
                goto bad;
            value = owned_instance;
            if (!PyExceptionInstance_Check(value)) {
                PyErr_Format(PyExc_TypeError,
                             "calling %R should have returned an instance of "
                             "BaseException, not %R",
                             type, Py_TYPE(value));
                goto bad;
            }
        }
    } else {
        PyErr_SetString(PyExc_TypeError,
            "raise: exception class must be a subclass of BaseException");
        goto bad;
    }
    if (cause) {
        PyObject *fixed_cause;
        if (cause == Py_None) {
            fixed_cause = NULL;
        } else if (PyExceptionClass_Check(cause)) {
            fixed_cause = PyObject_CallObject(cause, NULL);
            if (fixed_cause == NULL)
                goto bad;
        } else if (PyExceptionInstance_Check(cause)) {
            fixed_cause = cause;
            Py_INCREF(fixed_cause);
        } else {
            PyErr_SetString(PyExc_TypeError,
                            "exception causes must derive from "
                            "BaseException");
            goto bad;
        }
        PyException_SetCause(value, fixed_cause);
    }
    PyErr_SetObject(type, value);
    if (tb) {
#if CYTHON_COMPILING_IN_PYPY
        PyObject *tmp_type, *tmp_value, *tmp_tb;
        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
        Py_INCREF(tb);
        PyErr_Restore(tmp_type, tmp_value, tb);
        Py_XDECREF(tmp_tb);
#else
        PyThreadState *tstate = __Pyx_PyThreadState_Current;
        PyObject* tmp_tb = tstate->curexc_traceback;
        if (tb != tmp_tb) {
            Py_INCREF(tb);
            tstate->curexc_traceback = tb;
            Py_XDECREF(tmp_tb);
        }
#endif
    }
bad:
    Py_XDECREF(owned_instance);
    return;
}
#endif

/* PyFunctionFastCall */
#if CYTHON_FAST_PYCALL
static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
                                               PyObject *globals) {
    PyFrameObject *f;
    PyThreadState *tstate = __Pyx_PyThreadState_Current;
    PyObject **fastlocals;
    Py_ssize_t i;
    PyObject *result;
    assert(globals != NULL);
    /* XXX Perhaps we should create a specialized
       PyFrame_New() that doesn't take locals, but does
       take builtins without sanity checking them.
       */
    assert(tstate != NULL);
    f = PyFrame_New(tstate, co, globals, NULL);
    if (f == NULL) {
        return NULL;
    }
    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
    for (i = 0; i < na; i++) {
        Py_INCREF(*args);
        fastlocals[i] = *args++;
    }
    result = PyEval_EvalFrameEx(f,0);
    ++tstate->recursion_depth;
    Py_DECREF(f);
    --tstate->recursion_depth;
    return result;
}
#if 1 || PY_VERSION_HEX < 0x030600B1
static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
    PyObject *globals = PyFunction_GET_GLOBALS(func);
    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
    PyObject *closure;
#if PY_MAJOR_VERSION >= 3
    PyObject *kwdefs;
#endif
    PyObject *kwtuple, **k;
    PyObject **d;
    Py_ssize_t nd;
    Py_ssize_t nk;
    PyObject *result;
    assert(kwargs == NULL || PyDict_Check(kwargs));
    nk = kwargs ? PyDict_Size(kwargs) : 0;
    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
        return NULL;
    }
    if (
#if PY_MAJOR_VERSION >= 3
            co->co_kwonlyargcount == 0 &&
#endif
            likely(kwargs == NULL || nk == 0) &&
            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
        if (argdefs == NULL && co->co_argcount == nargs) {
            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
            goto done;
        }
        else if (nargs == 0 && argdefs != NULL
                 && co->co_argcount == Py_SIZE(argdefs)) {
            /* function called with no arguments, but all parameters have
               a default value: use default values as arguments .*/
            args = &PyTuple_GET_ITEM(argdefs, 0);
            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
            goto done;
        }
    }
    if (kwargs != NULL) {
        Py_ssize_t pos, i;
        kwtuple = PyTuple_New(2 * nk);
        if (kwtuple == NULL) {
            result = NULL;
            goto done;
        }
        k = &PyTuple_GET_ITEM(kwtuple, 0);
        pos = i = 0;
        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
            Py_INCREF(k[i]);
            Py_INCREF(k[i+1]);
            i += 2;
        }
        nk = i / 2;
    }
    else {
        kwtuple = NULL;
        k = NULL;
    }
    closure = PyFunction_GET_CLOSURE(func);
#if PY_MAJOR_VERSION >= 3
    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
#endif
    if (argdefs != NULL) {
        d = &PyTuple_GET_ITEM(argdefs, 0);
        nd = Py_SIZE(argdefs);
    }
    else {
        d = NULL;
        nd = 0;
    }
#if PY_MAJOR_VERSION >= 3
    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
                               args, (int)nargs,
                               k, (int)nk,
                               d, (int)nd, kwdefs, closure);
#else
    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
                               args, (int)nargs,
                               k, (int)nk,
                               d, (int)nd, closure);
#endif
    Py_XDECREF(kwtuple);
done:
    Py_LeaveRecursiveCall();
    return result;
}
#endif
#endif

/* PyObjectCallMethO */
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
    PyObject *self, *result;
    PyCFunction cfunc;
    cfunc = PyCFunction_GET_FUNCTION(func);
    self = PyCFunction_GET_SELF(func);
    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
        return NULL;
    result = cfunc(self, arg);
    Py_LeaveRecursiveCall();
    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
        PyErr_SetString(
            PyExc_SystemError,
            "NULL result without error in PyObject_Call");
    }
    return result;
}
#endif

/* PyObjectCallNoArg */
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
#if CYTHON_FAST_PYCALL
    if (PyFunction_Check(func)) {
        return __Pyx_PyFunction_FastCall(func, NULL, 0);
    }
#endif
#ifdef __Pyx_CyFunction_USED
    if (likely(PyCFunction_Check(func) || __Pyx_CyFunction_Check(func)))
#else
    if (likely(PyCFunction_Check(func)))
#endif
    {
        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
            return __Pyx_PyObject_CallMethO(func, NULL);
        }
    }
    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
}
#endif

/* PyCFunctionFastCall */
#if CYTHON_FAST_PYCCALL
static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
    PyObject *self = PyCFunction_GET_SELF(func);
    int flags = PyCFunction_GET_FLAGS(func);
    assert(PyCFunction_Check(func));
    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));
    assert(nargs >= 0);
    assert(nargs == 0 || args != NULL);
    /* _PyCFunction_FastCallDict() must not be called with an exception set,
       because it may clear it (directly or indirectly) and so the
       caller loses its exception */
    assert(!PyErr_Occurred());
    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);
    } else {
        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);
    }
}
#endif

/* PyObjectCallOneArg */
#if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
    PyObject *result;
    PyObject *args = PyTuple_New(1);
    if (unlikely(!args)) return NULL;
    Py_INCREF(arg);
    PyTuple_SET_ITEM(args, 0, arg);
    result = __Pyx_PyObject_Call(func, args, NULL);
    Py_DECREF(args);
    return result;
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
#if CYTHON_FAST_PYCALL
    if (PyFunction_Check(func)) {
        return __Pyx_PyFunction_FastCall(func, &arg, 1);
    }
#endif
    if (likely(PyCFunction_Check(func))) {
        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
            return __Pyx_PyObject_CallMethO(func, arg);
#if CYTHON_FAST_PYCCALL
        } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
#endif
        }
    }
    return __Pyx__PyObject_CallOneArg(func, arg);
}
#else
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
    PyObject *result;
    PyObject *args = PyTuple_Pack(1, arg);
    if (unlikely(!args)) return NULL;
    result = __Pyx_PyObject_Call(func, args, NULL);
    Py_DECREF(args);
    return result;
}
#endif

/* PyObjectCall2Args */
static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
    PyObject *args, *result = NULL;
    #if CYTHON_FAST_PYCALL
    if (PyFunction_Check(function)) {
        PyObject *args[2] = {arg1, arg2};
        return __Pyx_PyFunction_FastCall(function, args, 2);
    }
    #endif
    #if CYTHON_FAST_PYCCALL
    if (__Pyx_PyFastCFunction_Check(function)) {
        PyObject *args[2] = {arg1, arg2};
        return __Pyx_PyCFunction_FastCall(function, args, 2);
    }
    #endif
    args = PyTuple_New(2);
    if (unlikely(!args)) goto done;
    Py_INCREF(arg1);
    PyTuple_SET_ITEM(args, 0, arg1);
    Py_INCREF(arg2);
    PyTuple_SET_ITEM(args, 1, arg2);
    Py_INCREF(function);
    result = __Pyx_PyObject_Call(function, args, NULL);
    Py_DECREF(args);
    Py_DECREF(function);
done:
    return result;
}

/* PyObjectSetAttrStr */
#if CYTHON_USE_TYPE_SLOTS
static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
    PyTypeObject* tp = Py_TYPE(obj);
    if (likely(tp->tp_setattro))
        return tp->tp_setattro(obj, attr_name, value);
#if PY_MAJOR_VERSION < 3
    if (likely(tp->tp_setattr))
        return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value);
#endif
    return PyObject_SetAttr(obj, attr_name, value);
}
#endif

/* Import */
static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
    PyObject *empty_list = 0;
    PyObject *module = 0;
    PyObject *global_dict = 0;
    PyObject *empty_dict = 0;
    PyObject *list;
    #if PY_MAJOR_VERSION < 3
    PyObject *py_import;
    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
    if (!py_import)
        goto bad;
    #endif
    if (from_list)
        list = from_list;
    else {
        empty_list = PyList_New(0);
        if (!empty_list)
            goto bad;
        list = empty_list;
    }
    global_dict = PyModule_GetDict(__pyx_m);
    if (!global_dict)
        goto bad;
    empty_dict = PyDict_New();
    if (!empty_dict)
        goto bad;
    {
        #if PY_MAJOR_VERSION >= 3
        if (level == -1) {
            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
                module = PyImport_ImportModuleLevelObject(
                    name, global_dict, empty_dict, list, 1);
                if (!module) {
                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
                        goto bad;
                    PyErr_Clear();
                }
            }
            level = 0;
        }
        #endif
        if (!module) {
            #if PY_MAJOR_VERSION < 3
            PyObject *py_level = PyInt_FromLong(level);
            if (!py_level)
                goto bad;
            module = PyObject_CallFunctionObjArgs(py_import,
                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
            Py_DECREF(py_level);
            #else
            module = PyImport_ImportModuleLevelObject(
                name, global_dict, empty_dict, list, level);
            #endif
        }
    }
bad:
    #if PY_MAJOR_VERSION < 3
    Py_XDECREF(py_import);
    #endif
    Py_XDECREF(empty_list);
    Py_XDECREF(empty_dict);
    return module;
}

/* ImportFrom */
static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
        PyErr_Format(PyExc_ImportError,
        #if PY_MAJOR_VERSION < 3
            "cannot import name %.230s", PyString_AS_STRING(name));
        #else
            "cannot import name %S", name);
        #endif
    }
    return value;
}

/* DictGetItem */
#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
    PyObject *value;
    value = PyDict_GetItemWithError(d, key);
    if (unlikely(!value)) {
        if (!PyErr_Occurred()) {
            if (unlikely(PyTuple_Check(key))) {
                PyObject* args = PyTuple_Pack(1, key);
                if (likely(args)) {
                    PyErr_SetObject(PyExc_KeyError, args);
                    Py_DECREF(args);
                }
            } else {
                PyErr_SetObject(PyExc_KeyError, key);
            }
        }
        return NULL;
    }
    Py_INCREF(value);
    return value;
}
#endif

/* PyDictVersioning */
#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
    PyObject *dict = Py_TYPE(obj)->tp_dict;
    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
}
static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
    PyObject **dictptr = NULL;
    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
    if (offset) {
#if CYTHON_COMPILING_IN_CPYTHON
        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
#else
        dictptr = _PyObject_GetDictPtr(obj);
#endif
    }
    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
}
static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
    PyObject *dict = Py_TYPE(obj)->tp_dict;
    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
        return 0;
    return obj_dict_version == __Pyx_get_object_dict_version(obj);
}
#endif

/* GetModuleGlobalName */
#if CYTHON_USE_DICT_VERSIONS
static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
#else
static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
#endif
{
    PyObject *result;
#if !CYTHON_AVOID_BORROWED_REFS
#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
    if (likely(result)) {
        return __Pyx_NewRef(result);
    } else if (unlikely(PyErr_Occurred())) {
        return NULL;
    }
#else
    result = PyDict_GetItem(__pyx_d, name);
    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
    if (likely(result)) {
        return __Pyx_NewRef(result);
    }
#endif
#else
    result = PyObject_GetItem(__pyx_d, name);
    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
    if (likely(result)) {
        return __Pyx_NewRef(result);
    }
    PyErr_Clear();
#endif
    return __Pyx_GetBuiltinName(name);
}

/* BytesEquals */
static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
#if CYTHON_COMPILING_IN_PYPY
    return PyObject_RichCompareBool(s1, s2, equals);
#else
    if (s1 == s2) {
        return (equals == Py_EQ);
    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
        const char *ps1, *ps2;
        Py_ssize_t length = PyBytes_GET_SIZE(s1);
        if (length != PyBytes_GET_SIZE(s2))
            return (equals == Py_NE);
        ps1 = PyBytes_AS_STRING(s1);
        ps2 = PyBytes_AS_STRING(s2);
        if (ps1[0] != ps2[0]) {
            return (equals == Py_NE);
        } else if (length == 1) {
            return (equals == Py_EQ);
        } else {
            int result;
#if CYTHON_USE_UNICODE_INTERNALS
            Py_hash_t hash1, hash2;
            hash1 = ((PyBytesObject*)s1)->ob_shash;
            hash2 = ((PyBytesObject*)s2)->ob_shash;
            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
                return (equals == Py_NE);
            }
#endif
            result = memcmp(ps1, ps2, (size_t)length);
            return (equals == Py_EQ) ? (result == 0) : (result != 0);
        }
    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
        return (equals == Py_NE);
    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
        return (equals == Py_NE);
    } else {
        int result;
        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
        if (!py_result)
            return -1;
        result = __Pyx_PyObject_IsTrue(py_result);
        Py_DECREF(py_result);
        return result;
    }
#endif
}

/* UnicodeEquals */
static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
#if CYTHON_COMPILING_IN_PYPY
    return PyObject_RichCompareBool(s1, s2, equals);
#else
#if PY_MAJOR_VERSION < 3
    PyObject* owned_ref = NULL;
#endif
    int s1_is_unicode, s2_is_unicode;
    if (s1 == s2) {
        goto return_eq;
    }
    s1_is_unicode = PyUnicode_CheckExact(s1);
    s2_is_unicode = PyUnicode_CheckExact(s2);
#if PY_MAJOR_VERSION < 3
    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
        owned_ref = PyUnicode_FromObject(s2);
        if (unlikely(!owned_ref))
            return -1;
        s2 = owned_ref;
        s2_is_unicode = 1;
    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
        owned_ref = PyUnicode_FromObject(s1);
        if (unlikely(!owned_ref))
            return -1;
        s1 = owned_ref;
        s1_is_unicode = 1;
    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
        return __Pyx_PyBytes_Equals(s1, s2, equals);
    }
#endif
    if (s1_is_unicode & s2_is_unicode) {
        Py_ssize_t length;
        int kind;
        void *data1, *data2;
        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
            return -1;
        length = __Pyx_PyUnicode_GET_LENGTH(s1);
        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
            goto return_ne;
        }
#if CYTHON_USE_UNICODE_INTERNALS
        {
            Py_hash_t hash1, hash2;
        #if CYTHON_PEP393_ENABLED
            hash1 = ((PyASCIIObject*)s1)->hash;
            hash2 = ((PyASCIIObject*)s2)->hash;
        #else
            hash1 = ((PyUnicodeObject*)s1)->hash;
            hash2 = ((PyUnicodeObject*)s2)->hash;
        #endif
            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
                goto return_ne;
            }
        }
#endif
        kind = __Pyx_PyUnicode_KIND(s1);
        if (kind != __Pyx_PyUnicode_KIND(s2)) {
            goto return_ne;
        }
        data1 = __Pyx_PyUnicode_DATA(s1);
        data2 = __Pyx_PyUnicode_DATA(s2);
        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
            goto return_ne;
        } else if (length == 1) {
            goto return_eq;
        } else {
            int result = memcmp(data1, data2, (size_t)(length * kind));
            #if PY_MAJOR_VERSION < 3
            Py_XDECREF(owned_ref);
            #endif
            return (equals == Py_EQ) ? (result == 0) : (result != 0);
        }
    } else if ((s1 == Py_None) & s2_is_unicode) {
        goto return_ne;
    } else if ((s2 == Py_None) & s1_is_unicode) {
        goto return_ne;
    } else {
        int result;
        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
        #if PY_MAJOR_VERSION < 3
        Py_XDECREF(owned_ref);
        #endif
        if (!py_result)
            return -1;
        result = __Pyx_PyObject_IsTrue(py_result);
        Py_DECREF(py_result);
        return result;
    }
return_eq:
    #if PY_MAJOR_VERSION < 3
    Py_XDECREF(owned_ref);
    #endif
    return (equals == Py_EQ);
return_ne:
    #if PY_MAJOR_VERSION < 3
    Py_XDECREF(owned_ref);
    #endif
    return (equals == Py_NE);
#endif
}

/* SliceObject */
static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj,
        Py_ssize_t cstart, Py_ssize_t cstop,
        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
        int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
#if CYTHON_USE_TYPE_SLOTS
    PyMappingMethods* mp;
#if PY_MAJOR_VERSION < 3
    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
    if (likely(ms && ms->sq_slice)) {
        if (!has_cstart) {
            if (_py_start && (*_py_start != Py_None)) {
                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
            } else
                cstart = 0;
        }
        if (!has_cstop) {
            if (_py_stop && (*_py_stop != Py_None)) {
                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
            } else
                cstop = PY_SSIZE_T_MAX;
        }
        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
            Py_ssize_t l = ms->sq_length(obj);
            if (likely(l >= 0)) {
                if (cstop < 0) {
                    cstop += l;
                    if (cstop < 0) cstop = 0;
                }
                if (cstart < 0) {
                    cstart += l;
                    if (cstart < 0) cstart = 0;
                }
            } else {
                if (!PyErr_ExceptionMatches(PyExc_OverflowError))
                    goto bad;
                PyErr_Clear();
            }
        }
        return ms->sq_slice(obj, cstart, cstop);
    }
#endif
    mp = Py_TYPE(obj)->tp_as_mapping;
    if (likely(mp && mp->mp_subscript))
#endif
    {
        PyObject* result;
        PyObject *py_slice, *py_start, *py_stop;
        if (_py_slice) {
            py_slice = *_py_slice;
        } else {
            PyObject* owned_start = NULL;
            PyObject* owned_stop = NULL;
            if (_py_start) {
                py_start = *_py_start;
            } else {
                if (has_cstart) {
                    owned_start = py_start = PyInt_FromSsize_t(cstart);
                    if (unlikely(!py_start)) goto bad;
                } else
                    py_start = Py_None;
            }
            if (_py_stop) {
                py_stop = *_py_stop;
            } else {
                if (has_cstop) {
                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
                    if (unlikely(!py_stop)) {
                        Py_XDECREF(owned_start);
                        goto bad;
                    }
                } else
                    py_stop = Py_None;
            }
            py_slice = PySlice_New(py_start, py_stop, Py_None);
            Py_XDECREF(owned_start);
            Py_XDECREF(owned_stop);
            if (unlikely(!py_slice)) goto bad;
        }
#if CYTHON_USE_TYPE_SLOTS
        result = mp->mp_subscript(obj, py_slice);
#else
        result = PyObject_GetItem(obj, py_slice);
#endif
        if (!_py_slice) {
            Py_DECREF(py_slice);
        }
        return result;
    }
    PyErr_Format(PyExc_TypeError,
        "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name);
bad:
    return NULL;
}

/* PyIntCompare */
static CYTHON_INLINE PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED long inplace) {
    if (op1 == op2) {
        Py_RETURN_TRUE;
    }
    #if PY_MAJOR_VERSION < 3
    if (likely(PyInt_CheckExact(op1))) {
        const long b = intval;
        long a = PyInt_AS_LONG(op1);
        if (a == b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
    }
    #endif
    #if CYTHON_USE_PYLONG_INTERNALS
    if (likely(PyLong_CheckExact(op1))) {
        int unequal;
        unsigned long uintval;
        Py_ssize_t size = Py_SIZE(op1);
        const digit* digits = ((PyLongObject*)op1)->ob_digit;
        if (intval == 0) {
            if (size == 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
        } else if (intval < 0) {
            if (size >= 0)
                Py_RETURN_FALSE;
            intval = -intval;
            size = -size;
        } else {
            if (size <= 0)
                Py_RETURN_FALSE;
        }
        uintval = (unsigned long) intval;
#if PyLong_SHIFT * 4 < SIZEOF_LONG*8
        if (uintval >> (PyLong_SHIFT * 4)) {
            unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
        } else
#endif
#if PyLong_SHIFT * 3 < SIZEOF_LONG*8
        if (uintval >> (PyLong_SHIFT * 3)) {
            unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
        } else
#endif
#if PyLong_SHIFT * 2 < SIZEOF_LONG*8
        if (uintval >> (PyLong_SHIFT * 2)) {
            unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
        } else
#endif
#if PyLong_SHIFT * 1 < SIZEOF_LONG*8
        if (uintval >> (PyLong_SHIFT * 1)) {
            unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
        } else
#endif
            unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK));
        if (unequal == 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
    }
    #endif
    if (PyFloat_CheckExact(op1)) {
        const long b = intval;
        double a = PyFloat_AS_DOUBLE(op1);
        if ((double)a == (double)b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
    }
    return (
        PyObject_RichCompare(op1, op2, Py_EQ));
}

/* GetTopmostException */
#if CYTHON_USE_EXC_INFO_STACK
static _PyErr_StackItem *
__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
{
    _PyErr_StackItem *exc_info = tstate->exc_info;
    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
           exc_info->previous_item != NULL)
    {
        exc_info = exc_info->previous_item;
    }
    return exc_info;
}
#endif

/* SaveResetException */
#if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    #if CYTHON_USE_EXC_INFO_STACK
    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
    *type = exc_info->exc_type;
    *value = exc_info->exc_value;
    *tb = exc_info->exc_traceback;
    #else
    *type = tstate->exc_type;
    *value = tstate->exc_value;
    *tb = tstate->exc_traceback;
    #endif
    Py_XINCREF(*type);
    Py_XINCREF(*value);
    Py_XINCREF(*tb);
}
static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    #if CYTHON_USE_EXC_INFO_STACK
    _PyErr_StackItem *exc_info = tstate->exc_info;
    tmp_type = exc_info->exc_type;
    tmp_value = exc_info->exc_value;
    tmp_tb = exc_info->exc_traceback;
    exc_info->exc_type = type;
    exc_info->exc_value = value;
    exc_info->exc_traceback = tb;
    #else
    tmp_type = tstate->exc_type;
    tmp_value = tstate->exc_value;
    tmp_tb = tstate->exc_traceback;
    tstate->exc_type = type;
    tstate->exc_value = value;
    tstate->exc_traceback = tb;
    #endif
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
}
#endif

/* PyErrExceptionMatches */
#if CYTHON_FAST_THREAD_STATE
static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
    Py_ssize_t i, n;
    n = PyTuple_GET_SIZE(tuple);
#if PY_MAJOR_VERSION >= 3
    for (i=0; i<n; i++) {
        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
    }
#endif
    for (i=0; i<n; i++) {
        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
    }
    return 0;
}
static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
    PyObject *exc_type = tstate->curexc_type;
    if (exc_type == err) return 1;
    if (unlikely(!exc_type)) return 0;
    if (unlikely(PyTuple_Check(err)))
        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
}
#endif

/* GetException */
#if CYTHON_FAST_THREAD_STATE
static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
#else
static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
#endif
{
    PyObject *local_type, *local_value, *local_tb;
#if CYTHON_FAST_THREAD_STATE
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    local_type = tstate->curexc_type;
    local_value = tstate->curexc_value;
    local_tb = tstate->curexc_traceback;
    tstate->curexc_type = 0;
    tstate->curexc_value = 0;
    tstate->curexc_traceback = 0;
#else
    PyErr_Fetch(&local_type, &local_value, &local_tb);
#endif
    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
#if CYTHON_FAST_THREAD_STATE
    if (unlikely(tstate->curexc_type))
#else
    if (unlikely(PyErr_Occurred()))
#endif
        goto bad;
    #if PY_MAJOR_VERSION >= 3
    if (local_tb) {
        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
            goto bad;
    }
    #endif
    Py_XINCREF(local_tb);
    Py_XINCREF(local_type);
    Py_XINCREF(local_value);
    *type = local_type;
    *value = local_value;
    *tb = local_tb;
#if CYTHON_FAST_THREAD_STATE
    #if CYTHON_USE_EXC_INFO_STACK
    {
        _PyErr_StackItem *exc_info = tstate->exc_info;
        tmp_type = exc_info->exc_type;
        tmp_value = exc_info->exc_value;
        tmp_tb = exc_info->exc_traceback;
        exc_info->exc_type = local_type;
        exc_info->exc_value = local_value;
        exc_info->exc_traceback = local_tb;
    }
    #else
    tmp_type = tstate->exc_type;
    tmp_value = tstate->exc_value;
    tmp_tb = tstate->exc_traceback;
    tstate->exc_type = local_type;
    tstate->exc_value = local_value;
    tstate->exc_traceback = local_tb;
    #endif
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
#else
    PyErr_SetExcInfo(local_type, local_value, local_tb);
#endif
    return 0;
bad:
    *type = 0;
    *value = 0;
    *tb = 0;
    Py_XDECREF(local_type);
    Py_XDECREF(local_value);
    Py_XDECREF(local_tb);
    return -1;
}

/* PyIntCompare */
static CYTHON_INLINE PyObject* __Pyx_PyInt_NeObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED long inplace) {
    if (op1 == op2) {
        Py_RETURN_FALSE;
    }
    #if PY_MAJOR_VERSION < 3
    if (likely(PyInt_CheckExact(op1))) {
        const long b = intval;
        long a = PyInt_AS_LONG(op1);
        if (a != b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
    }
    #endif
    #if CYTHON_USE_PYLONG_INTERNALS
    if (likely(PyLong_CheckExact(op1))) {
        int unequal;
        unsigned long uintval;
        Py_ssize_t size = Py_SIZE(op1);
        const digit* digits = ((PyLongObject*)op1)->ob_digit;
        if (intval == 0) {
            if (size != 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
        } else if (intval < 0) {
            if (size >= 0)
                Py_RETURN_TRUE;
            intval = -intval;
            size = -size;
        } else {
            if (size <= 0)
                Py_RETURN_TRUE;
        }
        uintval = (unsigned long) intval;
#if PyLong_SHIFT * 4 < SIZEOF_LONG*8
        if (uintval >> (PyLong_SHIFT * 4)) {
            unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
        } else
#endif
#if PyLong_SHIFT * 3 < SIZEOF_LONG*8
        if (uintval >> (PyLong_SHIFT * 3)) {
            unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
        } else
#endif
#if PyLong_SHIFT * 2 < SIZEOF_LONG*8
        if (uintval >> (PyLong_SHIFT * 2)) {
            unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
        } else
#endif
#if PyLong_SHIFT * 1 < SIZEOF_LONG*8
        if (uintval >> (PyLong_SHIFT * 1)) {
            unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
                 | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
        } else
#endif
            unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK));
        if (unequal != 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
    }
    #endif
    if (PyFloat_CheckExact(op1)) {
        const long b = intval;
        double a = PyFloat_AS_DOUBLE(op1);
        if ((double)a != (double)b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
    }
    return (
        PyObject_RichCompare(op1, op2, Py_NE));
}

/* GetItemInt */
static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
    PyObject *r;
    if (!j) return NULL;
    r = PyObject_GetItem(o, j);
    Py_DECREF(j);
    return r;
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
                                                              CYTHON_NCP_UNUSED int wraparound,
                                                              CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
    Py_ssize_t wrapped_i = i;
    if (wraparound & unlikely(i < 0)) {
        wrapped_i += PyList_GET_SIZE(o);
    }
    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
        Py_INCREF(r);
        return r;
    }
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
    return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
                                                              CYTHON_NCP_UNUSED int wraparound,
                                                              CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
    Py_ssize_t wrapped_i = i;
    if (wraparound & unlikely(i < 0)) {
        wrapped_i += PyTuple_GET_SIZE(o);
    }
    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
        Py_INCREF(r);
        return r;
    }
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
    return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
                                                     CYTHON_NCP_UNUSED int wraparound,
                                                     CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
    if (is_list || PyList_CheckExact(o)) {
        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
            PyObject *r = PyList_GET_ITEM(o, n);
            Py_INCREF(r);
            return r;
        }
    }
    else if (PyTuple_CheckExact(o)) {
        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
            PyObject *r = PyTuple_GET_ITEM(o, n);
            Py_INCREF(r);
            return r;
        }
    } else {
        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
        if (likely(m && m->sq_item)) {
            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
                Py_ssize_t l = m->sq_length(o);
                if (likely(l >= 0)) {
                    i += l;
                } else {
                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
                        return NULL;
                    PyErr_Clear();
                }
            }
            return m->sq_item(o, i);
        }
    }
#else
    if (is_list || PySequence_Check(o)) {
        return PySequence_GetItem(o, i);
    }
#endif
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
}

/* ObjectGetItem */
#if CYTHON_USE_TYPE_SLOTS
static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
    PyObject *runerr;
    Py_ssize_t key_value;
    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
    if (unlikely(!(m && m->sq_item))) {
        PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
        return NULL;
    }
    key_value = __Pyx_PyIndex_AsSsize_t(index);
    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
    }
    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
        PyErr_Clear();
        PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
    }
    return NULL;
}
static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
    if (likely(m && m->mp_subscript)) {
        return m->mp_subscript(obj, key);
    }
    return __Pyx_PyObject_GetIndex(obj, key);
}
#endif

/* ExtTypeTest */
static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
    if (unlikely(!type)) {
        PyErr_SetString(PyExc_SystemError, "Missing type object");
        return 0;
    }
    if (likely(__Pyx_TypeCheck(obj, type)))
        return 1;
    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
                 Py_TYPE(obj)->tp_name, type->tp_name);
    return 0;
}

/* RaiseTooManyValuesToUnpack */
static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
    PyErr_Format(PyExc_ValueError,
                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
}

/* RaiseNeedMoreValuesToUnpack */
static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
    PyErr_Format(PyExc_ValueError,
                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
                 index, (index == 1) ? "" : "s");
}

/* IterFinish */
static CYTHON_INLINE int __Pyx_IterFinish(void) {
#if CYTHON_FAST_THREAD_STATE
    PyThreadState *tstate = __Pyx_PyThreadState_Current;
    PyObject* exc_type = tstate->curexc_type;
    if (unlikely(exc_type)) {
        if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) {
            PyObject *exc_value, *exc_tb;
            exc_value = tstate->curexc_value;
            exc_tb = tstate->curexc_traceback;
            tstate->curexc_type = 0;
            tstate->curexc_value = 0;
            tstate->curexc_traceback = 0;
            Py_DECREF(exc_type);
            Py_XDECREF(exc_value);
            Py_XDECREF(exc_tb);
            return 0;
        } else {
            return -1;
        }
    }
    return 0;
#else
    if (unlikely(PyErr_Occurred())) {
        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
            PyErr_Clear();
            return 0;
        } else {
            return -1;
        }
    }
    return 0;
#endif
}

/* UnpackItemEndCheck */
static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
    if (unlikely(retval)) {
        Py_DECREF(retval);
        __Pyx_RaiseTooManyValuesError(expected);
        return -1;
    } else {
        return __Pyx_IterFinish();
    }
    return 0;
}

/* SliceObject */
static CYTHON_INLINE int __Pyx_PyObject_SetSlice(PyObject* obj, PyObject* value,
        Py_ssize_t cstart, Py_ssize_t cstop,
        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
        int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
#if CYTHON_USE_TYPE_SLOTS
    PyMappingMethods* mp;
#if PY_MAJOR_VERSION < 3
    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
    if (likely(ms && ms->sq_ass_slice)) {
        if (!has_cstart) {
            if (_py_start && (*_py_start != Py_None)) {
                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
            } else
                cstart = 0;
        }
        if (!has_cstop) {
            if (_py_stop && (*_py_stop != Py_None)) {
                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
            } else
                cstop = PY_SSIZE_T_MAX;
        }
        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
            Py_ssize_t l = ms->sq_length(obj);
            if (likely(l >= 0)) {
                if (cstop < 0) {
                    cstop += l;
                    if (cstop < 0) cstop = 0;
                }
                if (cstart < 0) {
                    cstart += l;
                    if (cstart < 0) cstart = 0;
                }
            } else {
                if (!PyErr_ExceptionMatches(PyExc_OverflowError))
                    goto bad;
                PyErr_Clear();
            }
        }
        return ms->sq_ass_slice(obj, cstart, cstop, value);
    }
#endif
    mp = Py_TYPE(obj)->tp_as_mapping;
    if (likely(mp && mp->mp_ass_subscript))
#endif
    {
        int result;
        PyObject *py_slice, *py_start, *py_stop;
        if (_py_slice) {
            py_slice = *_py_slice;
        } else {
            PyObject* owned_start = NULL;
            PyObject* owned_stop = NULL;
            if (_py_start) {
                py_start = *_py_start;
            } else {
                if (has_cstart) {
                    owned_start = py_start = PyInt_FromSsize_t(cstart);
                    if (unlikely(!py_start)) goto bad;
                } else
                    py_start = Py_None;
            }
            if (_py_stop) {
                py_stop = *_py_stop;
            } else {
                if (has_cstop) {
                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
                    if (unlikely(!py_stop)) {
                        Py_XDECREF(owned_start);
                        goto bad;
                    }
                } else
                    py_stop = Py_None;
            }
            py_slice = PySlice_New(py_start, py_stop, Py_None);
            Py_XDECREF(owned_start);
            Py_XDECREF(owned_stop);
            if (unlikely(!py_slice)) goto bad;
        }
#if CYTHON_USE_TYPE_SLOTS
        result = mp->mp_ass_subscript(obj, py_slice, value);
#else
        result = value ? PyObject_SetItem(obj, py_slice, value) : PyObject_DelItem(obj, py_slice);
#endif
        if (!_py_slice) {
            Py_DECREF(py_slice);
        }
        return result;
    }
    PyErr_Format(PyExc_TypeError,
        "'%.200s' object does not support slice %.10s",
        Py_TYPE(obj)->tp_name, value ? "assignment" : "deletion");
bad:
    return -1;
}

/* MemviewSliceInit */
static int
__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
                        int ndim,
                        __Pyx_memviewslice *memviewslice,
                        int memview_is_new_reference)
{
    __Pyx_RefNannyDeclarations
    int i, retval=-1;
    Py_buffer *buf = &memview->view;
    __Pyx_RefNannySetupContext("init_memviewslice", 0);
    if (unlikely(memviewslice->memview || memviewslice->data)) {
        PyErr_SetString(PyExc_ValueError,
            "memviewslice is already initialized!");
        goto fail;
    }
    if (buf->strides) {
        for (i = 0; i < ndim; i++) {
            memviewslice->strides[i] = buf->strides[i];
        }
    } else {
        Py_ssize_t stride = buf->itemsize;
        for (i = ndim - 1; i >= 0; i--) {
            memviewslice->strides[i] = stride;
            stride *= buf->shape[i];
        }
    }
    for (i = 0; i < ndim; i++) {
        memviewslice->shape[i]   = buf->shape[i];
        if (buf->suboffsets) {
            memviewslice->suboffsets[i] = buf->suboffsets[i];
        } else {
            memviewslice->suboffsets[i] = -1;
        }
    }
    memviewslice->memview = memview;
    memviewslice->data = (char *)buf->buf;
    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
        Py_INCREF(memview);
    }
    retval = 0;
    goto no_fail;
fail:
    memviewslice->memview = 0;
    memviewslice->data = 0;
    retval = -1;
no_fail:
    __Pyx_RefNannyFinishContext();
    return retval;
}
#ifndef Py_NO_RETURN
#define Py_NO_RETURN
#endif
static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN {
    va_list vargs;
    char msg[200];
#ifdef HAVE_STDARG_PROTOTYPES
    va_start(vargs, fmt);
#else
    va_start(vargs);
#endif
    vsnprintf(msg, 200, fmt, vargs);
    va_end(vargs);
    Py_FatalError(msg);
}
static CYTHON_INLINE int
__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
                                   PyThread_type_lock lock)
{
    int result;
    PyThread_acquire_lock(lock, 1);
    result = (*acquisition_count)++;
    PyThread_release_lock(lock);
    return result;
}
static CYTHON_INLINE int
__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
                                   PyThread_type_lock lock)
{
    int result;
    PyThread_acquire_lock(lock, 1);
    result = (*acquisition_count)--;
    PyThread_release_lock(lock);
    return result;
}
static CYTHON_INLINE void
__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
{
    int first_time;
    struct __pyx_memoryview_obj *memview = memslice->memview;
    if (unlikely(!memview || (PyObject *) memview == Py_None))
        return;
    if (unlikely(__pyx_get_slice_count(memview) < 0))
        __pyx_fatalerror("Acquisition count is %d (line %d)",
                         __pyx_get_slice_count(memview), lineno);
    first_time = __pyx_add_acquisition_count(memview) == 0;
    if (unlikely(first_time)) {
        if (have_gil) {
            Py_INCREF((PyObject *) memview);
        } else {
            PyGILState_STATE _gilstate = PyGILState_Ensure();
            Py_INCREF((PyObject *) memview);
            PyGILState_Release(_gilstate);
        }
    }
}
static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,
                                             int have_gil, int lineno) {
    int last_time;
    struct __pyx_memoryview_obj *memview = memslice->memview;
    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
        memslice->memview = NULL;
        return;
    }
    if (unlikely(__pyx_get_slice_count(memview) <= 0))
        __pyx_fatalerror("Acquisition count is %d (line %d)",
                         __pyx_get_slice_count(memview), lineno);
    last_time = __pyx_sub_acquisition_count(memview) == 1;
    memslice->data = NULL;
    if (unlikely(last_time)) {
        if (have_gil) {
            Py_CLEAR(memslice->memview);
        } else {
            PyGILState_STATE _gilstate = PyGILState_Ensure();
            Py_CLEAR(memslice->memview);
            PyGILState_Release(_gilstate);
        }
    } else {
        memslice->memview = NULL;
    }
}

/* None */
static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
}

/* py_abs */
#if CYTHON_USE_PYLONG_INTERNALS
static PyObject *__Pyx_PyLong_AbsNeg(PyObject *n) {
    if (likely(Py_SIZE(n) == -1)) {
        return PyLong_FromLong(((PyLongObject*)n)->ob_digit[0]);
    }
#if CYTHON_COMPILING_IN_CPYTHON
    {
        PyObject *copy = _PyLong_Copy((PyLongObject*)n);
        if (likely(copy)) {
            __Pyx_SET_SIZE(copy, -Py_SIZE(copy));
        }
        return copy;
    }
#else
    return PyNumber_Negative(n);
#endif
}
#endif

/* MatrixMultiply */
#if PY_VERSION_HEX < 0x03050000
static PyObject* __Pyx_PyObject_CallMatrixMethod(PyObject* method, PyObject* arg) {
    PyObject *result = NULL;
#if CYTHON_UNPACK_METHODS
    if (likely(PyMethod_Check(method))) {
        PyObject *self = PyMethod_GET_SELF(method);
        if (likely(self)) {
            PyObject *args;
            PyObject *function = PyMethod_GET_FUNCTION(method);
            #if CYTHON_FAST_PYCALL
            if (PyFunction_Check(function)) {
                PyObject *args[2] = {self, arg};
                result = __Pyx_PyFunction_FastCall(function, args, 2);
                goto done;
            }
            #endif
            #if CYTHON_FAST_PYCCALL
            if (__Pyx_PyFastCFunction_Check(function)) {
                PyObject *args[2] = {self, arg};
                result = __Pyx_PyCFunction_FastCall(function, args, 2);
                goto done;
            }
            #endif
            args = PyTuple_New(2);
            if (unlikely(!args)) goto done;
            Py_INCREF(self);
            PyTuple_SET_ITEM(args, 0, self);
            Py_INCREF(arg);
            PyTuple_SET_ITEM(args, 1, arg);
            Py_INCREF(function);
            Py_DECREF(method); method = NULL;
            result = __Pyx_PyObject_Call(function, args, NULL);
            Py_DECREF(args);
            Py_DECREF(function);
            return result;
        }
    }
#endif
    result = __Pyx_PyObject_CallOneArg(method, arg);
done:
    Py_DECREF(method);
    return result;
}
#define __Pyx_TryMatrixMethod(x, y, py_method_name) {\
    PyObject *func = __Pyx_PyObject_GetAttrStr(x, py_method_name);\
    if (func) {\
        PyObject *result = __Pyx_PyObject_CallMatrixMethod(func, y);\
        if (result != Py_NotImplemented)\
            return result;\
        Py_DECREF(result);\
    } else {\
        if (!PyErr_ExceptionMatches(PyExc_AttributeError))\
            return NULL;\
        PyErr_Clear();\
    }\
}
static PyObject* __Pyx__PyNumber_MatrixMultiply(PyObject* x, PyObject* y, const char* op_name) {
    int right_is_subtype = PyObject_IsSubclass((PyObject*)Py_TYPE(y), (PyObject*)Py_TYPE(x));
    if (unlikely(right_is_subtype == -1))
        return NULL;
    if (right_is_subtype) {
        __Pyx_TryMatrixMethod(y, x, __pyx_n_s_rmatmul)
    }
    __Pyx_TryMatrixMethod(x, y, __pyx_n_s_matmul)
    if (!right_is_subtype) {
        __Pyx_TryMatrixMethod(y, x, __pyx_n_s_rmatmul)
    }
    PyErr_Format(PyExc_TypeError,
                 "unsupported operand type(s) for %.2s: '%.100s' and '%.100s'",
                 op_name,
                 Py_TYPE(x)->tp_name,
                 Py_TYPE(y)->tp_name);
    return NULL;
}
static PyObject* __Pyx_PyNumber_InPlaceMatrixMultiply(PyObject* x, PyObject* y) {
    __Pyx_TryMatrixMethod(x, y, __pyx_n_s_imatmul)
    return __Pyx__PyNumber_MatrixMultiply(x, y, "@=");
}
#undef __Pyx_TryMatrixMethod
#endif

/* SetItemInt */
static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
    int r;
    if (!j) return -1;
    r = PyObject_SetItem(o, j, v);
    Py_DECREF(j);
    return r;
}
static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list,
                                               CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
    if (is_list || PyList_CheckExact(o)) {
        Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o));
        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) {
            PyObject* old = PyList_GET_ITEM(o, n);
            Py_INCREF(v);
            PyList_SET_ITEM(o, n, v);
            Py_DECREF(old);
            return 1;
        }
    } else {
        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
        if (likely(m && m->sq_ass_item)) {
            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
                Py_ssize_t l = m->sq_length(o);
                if (likely(l >= 0)) {
                    i += l;
                } else {
                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
                        return -1;
                    PyErr_Clear();
                }
            }
            return m->sq_ass_item(o, i, v);
        }
    }
#else
#if CYTHON_COMPILING_IN_PYPY
    if (is_list || (PySequence_Check(o) && !PyDict_Check(o)))
#else
    if (is_list || PySequence_Check(o))
#endif
    {
        return PySequence_SetItem(o, i, v);
    }
#endif
    return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
}

/* ArgTypeTest */
static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
{
    if (unlikely(!type)) {
        PyErr_SetString(PyExc_SystemError, "Missing type object");
        return 0;
    }
    else if (exact) {
        #if PY_MAJOR_VERSION == 2
        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
        #endif
    }
    else {
        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
    }
    PyErr_Format(PyExc_TypeError,
        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
        name, type->tp_name, Py_TYPE(obj)->tp_name);
    return 0;
}

/* decode_c_string */
static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
         const char* encoding, const char* errors,
         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
    Py_ssize_t length;
    if (unlikely((start < 0) | (stop < 0))) {
        size_t slen = strlen(cstring);
        if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) {
            PyErr_SetString(PyExc_OverflowError,
                            "c-string too long to convert to Python");
            return NULL;
        }
        length = (Py_ssize_t) slen;
        if (start < 0) {
            start += length;
            if (start < 0)
                start = 0;
        }
        if (stop < 0)
            stop += length;
    }
    if (unlikely(stop <= start))
        return __Pyx_NewRef(__pyx_empty_unicode);
    length = stop - start;
    cstring += start;
    if (decode_func) {
        return decode_func(cstring, length, errors);
    } else {
        return PyUnicode_Decode(cstring, length, encoding, errors);
    }
}

/* GetAttr3 */
static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
    __Pyx_PyThreadState_declare
    __Pyx_PyThreadState_assign
    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
        return NULL;
    __Pyx_PyErr_Clear();
    Py_INCREF(d);
    return d;
}
static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
    PyObject *r = __Pyx_GetAttr(o, n);
    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
}

/* RaiseNoneIterError */
static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
}

/* SwapException */
#if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    #if CYTHON_USE_EXC_INFO_STACK
    _PyErr_StackItem *exc_info = tstate->exc_info;
    tmp_type = exc_info->exc_type;
    tmp_value = exc_info->exc_value;
    tmp_tb = exc_info->exc_traceback;
    exc_info->exc_type = *type;
    exc_info->exc_value = *value;
    exc_info->exc_traceback = *tb;
    #else
    tmp_type = tstate->exc_type;
    tmp_value = tstate->exc_value;
    tmp_tb = tstate->exc_traceback;
    tstate->exc_type = *type;
    tstate->exc_value = *value;
    tstate->exc_traceback = *tb;
    #endif
    *type = tmp_type;
    *value = tmp_value;
    *tb = tmp_tb;
}
#else
static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
    PyErr_SetExcInfo(*type, *value, *tb);
    *type = tmp_type;
    *value = tmp_value;
    *tb = tmp_tb;
}
#endif

/* FastTypeChecks */
#if CYTHON_COMPILING_IN_CPYTHON
static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
    while (a) {
        a = a->tp_base;
        if (a == b)
            return 1;
    }
    return b == &PyBaseObject_Type;
}
static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
    PyObject *mro;
    if (a == b) return 1;
    mro = a->tp_mro;
    if (likely(mro)) {
        Py_ssize_t i, n;
        n = PyTuple_GET_SIZE(mro);
        for (i = 0; i < n; i++) {
            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
                return 1;
        }
        return 0;
    }
    return __Pyx_InBases(a, b);
}
#if PY_MAJOR_VERSION == 2
static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
    PyObject *exception, *value, *tb;
    int res;
    __Pyx_PyThreadState_declare
    __Pyx_PyThreadState_assign
    __Pyx_ErrFetch(&exception, &value, &tb);
    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
    if (unlikely(res == -1)) {
        PyErr_WriteUnraisable(err);
        res = 0;
    }
    if (!res) {
        res = PyObject_IsSubclass(err, exc_type2);
        if (unlikely(res == -1)) {
            PyErr_WriteUnraisable(err);
            res = 0;
        }
    }
    __Pyx_ErrRestore(exception, value, tb);
    return res;
}
#else
static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
    if (!res) {
        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
    }
    return res;
}
#endif
static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
    Py_ssize_t i, n;
    assert(PyExceptionClass_Check(exc_type));
    n = PyTuple_GET_SIZE(tuple);
#if PY_MAJOR_VERSION >= 3
    for (i=0; i<n; i++) {
        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
    }
#endif
    for (i=0; i<n; i++) {
        PyObject *t = PyTuple_GET_ITEM(tuple, i);
        #if PY_MAJOR_VERSION < 3
        if (likely(exc_type == t)) return 1;
        #endif
        if (likely(PyExceptionClass_Check(t))) {
            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
        } else {
        }
    }
    return 0;
}
static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
    if (likely(err == exc_type)) return 1;
    if (likely(PyExceptionClass_Check(err))) {
        if (likely(PyExceptionClass_Check(exc_type))) {
            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
        } else if (likely(PyTuple_Check(exc_type))) {
            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
        } else {
        }
    }
    return PyErr_GivenExceptionMatches(err, exc_type);
}
static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
    assert(PyExceptionClass_Check(exc_type1));
    assert(PyExceptionClass_Check(exc_type2));
    if (likely(err == exc_type1 || err == exc_type2)) return 1;
    if (likely(PyExceptionClass_Check(err))) {
        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
    }
    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
}
#endif

/* PyIntBinop */
#if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {
    (void)inplace;
    (void)zerodivision_check;
    #if PY_MAJOR_VERSION < 3
    if (likely(PyInt_CheckExact(op1))) {
        const long b = intval;
        long x;
        long a = PyInt_AS_LONG(op1);
            x = (long)((unsigned long)a + b);
            if (likely((x^a) >= 0 || (x^b) >= 0))
                return PyInt_FromLong(x);
            return PyLong_Type.tp_as_number->nb_add(op1, op2);
    }
    #endif
    #if CYTHON_USE_PYLONG_INTERNALS
    if (likely(PyLong_CheckExact(op1))) {
        const long b = intval;
        long a, x;
#ifdef HAVE_LONG_LONG
        const PY_LONG_LONG llb = intval;
        PY_LONG_LONG lla, llx;
#endif
        const digit* digits = ((PyLongObject*)op1)->ob_digit;
        const Py_ssize_t size = Py_SIZE(op1);
        if (likely(__Pyx_sst_abs(size) <= 1)) {
            a = likely(size) ? digits[0] : 0;
            if (size == -1) a = -a;
        } else {
            switch (size) {
                case -2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                    CYTHON_FALLTHROUGH;
                case 2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                    CYTHON_FALLTHROUGH;
                case -3:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                    CYTHON_FALLTHROUGH;
                case 3:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                    CYTHON_FALLTHROUGH;
                case -4:
                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                    CYTHON_FALLTHROUGH;
                case 4:
                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                    CYTHON_FALLTHROUGH;
                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
            }
        }
                x = a + b;
            return PyLong_FromLong(x);
#ifdef HAVE_LONG_LONG
        long_long:
                llx = lla + llb;
            return PyLong_FromLongLong(llx);
#endif
        
        
    }
    #endif
    if (PyFloat_CheckExact(op1)) {
        const long b = intval;
        double a = PyFloat_AS_DOUBLE(op1);
            double result;
            PyFPE_START_PROTECT("add", return NULL)
            result = ((double)a) + (double)b;
            PyFPE_END_PROTECT(result)
            return PyFloat_FromDouble(result);
    }
    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
}
#endif

/* PyObject_GenericGetAttrNoDict */
#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
    PyErr_Format(PyExc_AttributeError,
#if PY_MAJOR_VERSION >= 3
                 "'%.50s' object has no attribute '%U'",
                 tp->tp_name, attr_name);
#else
                 "'%.50s' object has no attribute '%.400s'",
                 tp->tp_name, PyString_AS_STRING(attr_name));
#endif
    return NULL;
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
    PyObject *descr;
    PyTypeObject *tp = Py_TYPE(obj);
    if (unlikely(!PyString_Check(attr_name))) {
        return PyObject_GenericGetAttr(obj, attr_name);
    }
    assert(!tp->tp_dictoffset);
    descr = _PyType_Lookup(tp, attr_name);
    if (unlikely(!descr)) {
        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
    }
    Py_INCREF(descr);
    #if PY_MAJOR_VERSION < 3
    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
    #endif
    {
        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
        if (unlikely(f)) {
            PyObject *res = f(descr, obj, (PyObject *)tp);
            Py_DECREF(descr);
            return res;
        }
    }
    return descr;
}
#endif

/* PyObject_GenericGetAttr */
#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
        return PyObject_GenericGetAttr(obj, attr_name);
    }
    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
}
#endif

/* SetVTable */
static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
#if PY_VERSION_HEX >= 0x02070000
    PyObject *ob = PyCapsule_New(vtable, 0, 0);
#else
    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
#endif
    if (!ob)
        goto bad;
    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
        goto bad;
    Py_DECREF(ob);
    return 0;
bad:
    Py_XDECREF(ob);
    return -1;
}

/* PyObjectGetAttrStrNoError */
static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {
    __Pyx_PyThreadState_declare
    __Pyx_PyThreadState_assign
    if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
        __Pyx_PyErr_Clear();
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {
    PyObject *result;
#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1
    PyTypeObject* tp = Py_TYPE(obj);
    if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {
        return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1);
    }
#endif
    result = __Pyx_PyObject_GetAttrStr(obj, attr_name);
    if (unlikely(!result)) {
        __Pyx_PyObject_GetAttrStr_ClearAttributeError();
    }
    return result;
}

/* SetupReduce */
static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
  int ret;
  PyObject *name_attr;
  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name);
  if (likely(name_attr)) {
      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
  } else {
      ret = -1;
  }
  if (unlikely(ret < 0)) {
      PyErr_Clear();
      ret = 0;
  }
  Py_XDECREF(name_attr);
  return ret;
}
static int __Pyx_setup_reduce(PyObject* type_obj) {
    int ret = 0;
    PyObject *object_reduce = NULL;
    PyObject *object_reduce_ex = NULL;
    PyObject *reduce = NULL;
    PyObject *reduce_ex = NULL;
    PyObject *reduce_cython = NULL;
    PyObject *setstate = NULL;
    PyObject *setstate_cython = NULL;
#if CYTHON_USE_PYTYPE_LOOKUP
    if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
#else
    if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
#endif
#if CYTHON_USE_PYTYPE_LOOKUP
    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
#else
    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
#endif
    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
    if (reduce_ex == object_reduce_ex) {
#if CYTHON_USE_PYTYPE_LOOKUP
        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_2); if (!object_reduce) goto __PYX_BAD;
#else
        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_2); if (!object_reduce) goto __PYX_BAD;
#endif
        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_2); if (unlikely(!reduce)) goto __PYX_BAD;
        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
            if (likely(reduce_cython)) {
                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_2, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
            } else if (reduce == object_reduce || PyErr_Occurred()) {
                goto __PYX_BAD;
            }
            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
            if (!setstate) PyErr_Clear();
            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
                if (likely(setstate_cython)) {
                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
                } else if (!setstate || PyErr_Occurred()) {
                    goto __PYX_BAD;
                }
            }
            PyType_Modified((PyTypeObject*)type_obj);
        }
    }
    goto __PYX_GOOD;
__PYX_BAD:
    if (!PyErr_Occurred())
        PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
    ret = -1;
__PYX_GOOD:
#if !CYTHON_USE_PYTYPE_LOOKUP
    Py_XDECREF(object_reduce);
    Py_XDECREF(object_reduce_ex);
#endif
    Py_XDECREF(reduce);
    Py_XDECREF(reduce_ex);
    Py_XDECREF(reduce_cython);
    Py_XDECREF(setstate);
    Py_XDECREF(setstate_cython);
    return ret;
}

/* TypeImport */
#ifndef __PYX_HAVE_RT_ImportType
#define __PYX_HAVE_RT_ImportType
static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name,
    size_t size, enum __Pyx_ImportType_CheckSize check_size)
{
    PyObject *result = 0;
    char warning[200];
    Py_ssize_t basicsize;
#ifdef Py_LIMITED_API
    PyObject *py_basicsize;
#endif
    result = PyObject_GetAttrString(module, class_name);
    if (!result)
        goto bad;
    if (!PyType_Check(result)) {
        PyErr_Format(PyExc_TypeError,
            "%.200s.%.200s is not a type object",
            module_name, class_name);
        goto bad;
    }
#ifndef Py_LIMITED_API
    basicsize = ((PyTypeObject *)result)->tp_basicsize;
#else
    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
    if (!py_basicsize)
        goto bad;
    basicsize = PyLong_AsSsize_t(py_basicsize);
    Py_DECREF(py_basicsize);
    py_basicsize = 0;
    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
        goto bad;
#endif
    if ((size_t)basicsize < size) {
        PyErr_Format(PyExc_ValueError,
            "%.200s.%.200s size changed, may indicate binary incompatibility. "
            "Expected %zd from C header, got %zd from PyObject",
            module_name, class_name, size, basicsize);
        goto bad;
    }
    if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) {
        PyErr_Format(PyExc_ValueError,
            "%.200s.%.200s size changed, may indicate binary incompatibility. "
            "Expected %zd from C header, got %zd from PyObject",
            module_name, class_name, size, basicsize);
        goto bad;
    }
    else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) {
        PyOS_snprintf(warning, sizeof(warning),
            "%s.%s size changed, may indicate binary incompatibility. "
            "Expected %zd from C header, got %zd from PyObject",
            module_name, class_name, size, basicsize);
        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
    }
    return (PyTypeObject *)result;
bad:
    Py_XDECREF(result);
    return NULL;
}
#endif

/* GetVTable */
static void* __Pyx_GetVtable(PyObject *dict) {
    void* ptr;
    PyObject *ob = PyObject_GetItem(dict, __pyx_n_s_pyx_vtable);
    if (!ob)
        goto bad;
#if PY_VERSION_HEX >= 0x02070000
    ptr = PyCapsule_GetPointer(ob, 0);
#else
    ptr = PyCObject_AsVoidPtr(ob);
#endif
    if (!ptr && !PyErr_Occurred())
        PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type");
    Py_DECREF(ob);
    return ptr;
bad:
    Py_XDECREF(ob);
    return NULL;
}

/* CLineInTraceback */
#ifndef CYTHON_CLINE_IN_TRACEBACK
static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {
    PyObject *use_cline;
    PyObject *ptype, *pvalue, *ptraceback;
#if CYTHON_COMPILING_IN_CPYTHON
    PyObject **cython_runtime_dict;
#endif
    if (unlikely(!__pyx_cython_runtime)) {
        return c_line;
    }
    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
#if CYTHON_COMPILING_IN_CPYTHON
    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
    if (likely(cython_runtime_dict)) {
        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
            use_cline, *cython_runtime_dict,
            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
    } else
#endif
    {
      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
      if (use_cline_obj) {
        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
        Py_DECREF(use_cline_obj);
      } else {
        PyErr_Clear();
        use_cline = NULL;
      }
    }
    if (!use_cline) {
        c_line = 0;
        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
    }
    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
        c_line = 0;
    }
    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
    return c_line;
}
#endif

/* CodeObjectCache */
static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
    int start = 0, mid = 0, end = count - 1;
    if (end >= 0 && code_line > entries[end].code_line) {
        return count;
    }
    while (start < end) {
        mid = start + (end - start) / 2;
        if (code_line < entries[mid].code_line) {
            end = mid;
        } else if (code_line > entries[mid].code_line) {
             start = mid + 1;
        } else {
            return mid;
        }
    }
    if (code_line <= entries[mid].code_line) {
        return mid;
    } else {
        return mid + 1;
    }
}
static PyCodeObject *__pyx_find_code_object(int code_line) {
    PyCodeObject* code_object;
    int pos;
    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
        return NULL;
    }
    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
        return NULL;
    }
    code_object = __pyx_code_cache.entries[pos].code_object;
    Py_INCREF(code_object);
    return code_object;
}
static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
    int pos, i;
    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
    if (unlikely(!code_line)) {
        return;
    }
    if (unlikely(!entries)) {
        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
        if (likely(entries)) {
            __pyx_code_cache.entries = entries;
            __pyx_code_cache.max_count = 64;
            __pyx_code_cache.count = 1;
            entries[0].code_line = code_line;
            entries[0].code_object = code_object;
            Py_INCREF(code_object);
        }
        return;
    }
    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
        PyCodeObject* tmp = entries[pos].code_object;
        entries[pos].code_object = code_object;
        Py_DECREF(tmp);
        return;
    }
    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
        int new_max = __pyx_code_cache.max_count + 64;
        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
        if (unlikely(!entries)) {
            return;
        }
        __pyx_code_cache.entries = entries;
        __pyx_code_cache.max_count = new_max;
    }
    for (i=__pyx_code_cache.count; i>pos; i--) {
        entries[i] = entries[i-1];
    }
    entries[pos].code_line = code_line;
    entries[pos].code_object = code_object;
    __pyx_code_cache.count++;
    Py_INCREF(code_object);
}

/* AddTraceback */
#include "compile.h"
#include "frameobject.h"
#include "traceback.h"
static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
            const char *funcname, int c_line,
            int py_line, const char *filename) {
    PyCodeObject *py_code = 0;
    PyObject *py_srcfile = 0;
    PyObject *py_funcname = 0;
    #if PY_MAJOR_VERSION < 3
    py_srcfile = PyString_FromString(filename);
    #else
    py_srcfile = PyUnicode_FromString(filename);
    #endif
    if (!py_srcfile) goto bad;
    if (c_line) {
        #if PY_MAJOR_VERSION < 3
        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
        #else
        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
        #endif
    }
    else {
        #if PY_MAJOR_VERSION < 3
        py_funcname = PyString_FromString(funcname);
        #else
        py_funcname = PyUnicode_FromString(funcname);
        #endif
    }
    if (!py_funcname) goto bad;
    py_code = __Pyx_PyCode_New(
        0,
        0,
        0,
        0,
        0,
        __pyx_empty_bytes, /*PyObject *code,*/
        __pyx_empty_tuple, /*PyObject *consts,*/
        __pyx_empty_tuple, /*PyObject *names,*/
        __pyx_empty_tuple, /*PyObject *varnames,*/
        __pyx_empty_tuple, /*PyObject *freevars,*/
        __pyx_empty_tuple, /*PyObject *cellvars,*/
        py_srcfile,   /*PyObject *filename,*/
        py_funcname,  /*PyObject *name,*/
        py_line,
        __pyx_empty_bytes  /*PyObject *lnotab*/
    );
    Py_DECREF(py_srcfile);
    Py_DECREF(py_funcname);
    return py_code;
bad:
    Py_XDECREF(py_srcfile);
    Py_XDECREF(py_funcname);
    return NULL;
}
static void __Pyx_AddTraceback(const char *funcname, int c_line,
                               int py_line, const char *filename) {
    PyCodeObject *py_code = 0;
    PyFrameObject *py_frame = 0;
    PyThreadState *tstate = __Pyx_PyThreadState_Current;
    if (c_line) {
        c_line = __Pyx_CLineForTraceback(tstate, c_line);
    }
    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
    if (!py_code) {
        py_code = __Pyx_CreateCodeObjectForTraceback(
            funcname, c_line, py_line, filename);
        if (!py_code) goto bad;
        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
    }
    py_frame = PyFrame_New(
        tstate,            /*PyThreadState *tstate,*/
        py_code,           /*PyCodeObject *code,*/
        __pyx_d,    /*PyObject *globals,*/
        0                  /*PyObject *locals*/
    );
    if (!py_frame) goto bad;
    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
    PyTraceBack_Here(py_frame);
bad:
    Py_XDECREF(py_code);
    Py_XDECREF(py_frame);
}

#if PY_MAJOR_VERSION < 3
static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
    return -1;
}
static void __Pyx_ReleaseBuffer(Py_buffer *view) {
    PyObject *obj = view->obj;
    if (!obj) return;
    if (PyObject_CheckBuffer(obj)) {
        PyBuffer_Release(view);
        return;
    }
    if ((0)) {}
    view->obj = NULL;
    Py_DECREF(obj);
}
#endif


/* MemviewSliceIsContig */
static int
__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
{
    int i, index, step, start;
    Py_ssize_t itemsize = mvs.memview->view.itemsize;
    if (order == 'F') {
        step = 1;
        start = 0;
    } else {
        step = -1;
        start = ndim - 1;
    }
    for (i = 0; i < ndim; i++) {
        index = start + step * i;
        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
            return 0;
        itemsize *= mvs.shape[index];
    }
    return 1;
}

/* OverlappingSlices */
static void
__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
                               void **out_start, void **out_end,
                               int ndim, size_t itemsize)
{
    char *start, *end;
    int i;
    start = end = slice->data;
    for (i = 0; i < ndim; i++) {
        Py_ssize_t stride = slice->strides[i];
        Py_ssize_t extent = slice->shape[i];
        if (extent == 0) {
            *out_start = *out_end = start;
            return;
        } else {
            if (stride > 0)
                end += stride * (extent - 1);
            else
                start += stride * (extent - 1);
        }
    }
    *out_start = start;
    *out_end = end + itemsize;
}
static int
__pyx_slices_overlap(__Pyx_memviewslice *slice1,
                     __Pyx_memviewslice *slice2,
                     int ndim, size_t itemsize)
{
    void *start1, *end1, *start2, *end2;
    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
    return (start1 < end2) && (start2 < end1);
}

/* Capsule */
static CYTHON_INLINE PyObject *
__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
{
    PyObject *cobj;
#if PY_VERSION_HEX >= 0x02070000
    cobj = PyCapsule_New(p, sig, NULL);
#else
    cobj = PyCObject_FromVoidPtr(p, NULL);
#endif
    return cobj;
}

/* CIntFromPyVerify */
#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
    {\
        func_type value = func_value;\
        if (sizeof(target_type) < sizeof(func_type)) {\
            if (unlikely(value != (func_type) (target_type) value)) {\
                func_type zero = 0;\
                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
                    return (target_type) -1;\
                if (is_unsigned && unlikely(value < zero))\
                    goto raise_neg_overflow;\
                else\
                    goto raise_overflow;\
            }\
        }\
        return (target_type) value;\
    }

/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
    const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(long) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(long) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(long) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(long),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int64_t(int64_t value) {
    const int64_t neg_one = (int64_t) ((int64_t) 0 - (int64_t) 1), const_zero = (int64_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(int64_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int64_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(int64_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(int64_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(int64_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(int64_t),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint64_t(uint64_t value) {
    const uint64_t neg_one = (uint64_t) ((uint64_t) 0 - (uint64_t) 1), const_zero = (uint64_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(uint64_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(uint64_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(uint64_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(uint64_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(uint64_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(uint64_t),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) {
    const Py_intptr_t neg_one = (Py_intptr_t) ((Py_intptr_t) 0 - (Py_intptr_t) 1), const_zero = (Py_intptr_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(Py_intptr_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(Py_intptr_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t),
                                     little, !is_unsigned);
    }
}

/* Declarations */
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
      return ::std::complex< float >(x, y);
    }
  #else
    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
      return x + y*(__pyx_t_float_complex)_Complex_I;
    }
  #endif
#else
    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
      __pyx_t_float_complex z;
      z.real = x;
      z.imag = y;
      return z;
    }
#endif

/* Arithmetic */
#if CYTHON_CCOMPLEX
#else
    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
       return (a.real == b.real) && (a.imag == b.imag);
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        __pyx_t_float_complex z;
        z.real = a.real + b.real;
        z.imag = a.imag + b.imag;
        return z;
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        __pyx_t_float_complex z;
        z.real = a.real - b.real;
        z.imag = a.imag - b.imag;
        return z;
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        __pyx_t_float_complex z;
        z.real = a.real * b.real - a.imag * b.imag;
        z.imag = a.real * b.imag + a.imag * b.real;
        return z;
    }
    #if 1
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        if (b.imag == 0) {
            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
        } else if (fabsf(b.real) >= fabsf(b.imag)) {
            if (b.real == 0 && b.imag == 0) {
                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
            } else {
                float r = b.imag / b.real;
                float s = (float)(1.0) / (b.real + b.imag * r);
                return __pyx_t_float_complex_from_parts(
                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
            }
        } else {
            float r = b.real / b.imag;
            float s = (float)(1.0) / (b.imag + b.real * r);
            return __pyx_t_float_complex_from_parts(
                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
        }
    }
    #else
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        if (b.imag == 0) {
            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
        } else {
            float denom = b.real * b.real + b.imag * b.imag;
            return __pyx_t_float_complex_from_parts(
                (a.real * b.real + a.imag * b.imag) / denom,
                (a.imag * b.real - a.real * b.imag) / denom);
        }
    }
    #endif
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
        __pyx_t_float_complex z;
        z.real = -a.real;
        z.imag = -a.imag;
        return z;
    }
    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
       return (a.real == 0) && (a.imag == 0);
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
        __pyx_t_float_complex z;
        z.real =  a.real;
        z.imag = -a.imag;
        return z;
    }
    #if 1
        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
            return sqrtf(z.real*z.real + z.imag*z.imag);
          #else
            return hypotf(z.real, z.imag);
          #endif
        }
        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
            __pyx_t_float_complex z;
            float r, lnr, theta, z_r, z_theta;
            if (b.imag == 0 && b.real == (int)b.real) {
                if (b.real < 0) {
                    float denom = a.real * a.real + a.imag * a.imag;
                    a.real = a.real / denom;
                    a.imag = -a.imag / denom;
                    b.real = -b.real;
                }
                switch ((int)b.real) {
                    case 0:
                        z.real = 1;
                        z.imag = 0;
                        return z;
                    case 1:
                        return a;
                    case 2:
                        return __Pyx_c_prod_float(a, a);
                    case 3:
                        z = __Pyx_c_prod_float(a, a);
                        return __Pyx_c_prod_float(z, a);
                    case 4:
                        z = __Pyx_c_prod_float(a, a);
                        return __Pyx_c_prod_float(z, z);
                }
            }
            if (a.imag == 0) {
                if (a.real == 0) {
                    return a;
                } else if (b.imag == 0) {
                    z.real = powf(a.real, b.real);
                    z.imag = 0;
                    return z;
                } else if (a.real > 0) {
                    r = a.real;
                    theta = 0;
                } else {
                    r = -a.real;
                    theta = atan2f(0.0, -1.0);
                }
            } else {
                r = __Pyx_c_abs_float(a);
                theta = atan2f(a.imag, a.real);
            }
            lnr = logf(r);
            z_r = expf(lnr * b.real - theta * b.imag);
            z_theta = theta * b.real + lnr * b.imag;
            z.real = z_r * cosf(z_theta);
            z.imag = z_r * sinf(z_theta);
            return z;
        }
    #endif
#endif

/* Declarations */
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
      return ::std::complex< double >(x, y);
    }
  #else
    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
      return x + y*(__pyx_t_double_complex)_Complex_I;
    }
  #endif
#else
    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
      __pyx_t_double_complex z;
      z.real = x;
      z.imag = y;
      return z;
    }
#endif

/* Arithmetic */
#if CYTHON_CCOMPLEX
#else
    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
       return (a.real == b.real) && (a.imag == b.imag);
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        __pyx_t_double_complex z;
        z.real = a.real + b.real;
        z.imag = a.imag + b.imag;
        return z;
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        __pyx_t_double_complex z;
        z.real = a.real - b.real;
        z.imag = a.imag - b.imag;
        return z;
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        __pyx_t_double_complex z;
        z.real = a.real * b.real - a.imag * b.imag;
        z.imag = a.real * b.imag + a.imag * b.real;
        return z;
    }
    #if 1
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        if (b.imag == 0) {
            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
        } else if (fabs(b.real) >= fabs(b.imag)) {
            if (b.real == 0 && b.imag == 0) {
                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
            } else {
                double r = b.imag / b.real;
                double s = (double)(1.0) / (b.real + b.imag * r);
                return __pyx_t_double_complex_from_parts(
                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
            }
        } else {
            double r = b.real / b.imag;
            double s = (double)(1.0) / (b.imag + b.real * r);
            return __pyx_t_double_complex_from_parts(
                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
        }
    }
    #else
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        if (b.imag == 0) {
            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
        } else {
            double denom = b.real * b.real + b.imag * b.imag;
            return __pyx_t_double_complex_from_parts(
                (a.real * b.real + a.imag * b.imag) / denom,
                (a.imag * b.real - a.real * b.imag) / denom);
        }
    }
    #endif
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
        __pyx_t_double_complex z;
        z.real = -a.real;
        z.imag = -a.imag;
        return z;
    }
    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
       return (a.real == 0) && (a.imag == 0);
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
        __pyx_t_double_complex z;
        z.real =  a.real;
        z.imag = -a.imag;
        return z;
    }
    #if 1
        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
            return sqrt(z.real*z.real + z.imag*z.imag);
          #else
            return hypot(z.real, z.imag);
          #endif
        }
        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
            __pyx_t_double_complex z;
            double r, lnr, theta, z_r, z_theta;
            if (b.imag == 0 && b.real == (int)b.real) {
                if (b.real < 0) {
                    double denom = a.real * a.real + a.imag * a.imag;
                    a.real = a.real / denom;
                    a.imag = -a.imag / denom;
                    b.real = -b.real;
                }
                switch ((int)b.real) {
                    case 0:
                        z.real = 1;
                        z.imag = 0;
                        return z;
                    case 1:
                        return a;
                    case 2:
                        return __Pyx_c_prod_double(a, a);
                    case 3:
                        z = __Pyx_c_prod_double(a, a);
                        return __Pyx_c_prod_double(z, a);
                    case 4:
                        z = __Pyx_c_prod_double(a, a);
                        return __Pyx_c_prod_double(z, z);
                }
            }
            if (a.imag == 0) {
                if (a.real == 0) {
                    return a;
                } else if (b.imag == 0) {
                    z.real = pow(a.real, b.real);
                    z.imag = 0;
                    return z;
                } else if (a.real > 0) {
                    r = a.real;
                    theta = 0;
                } else {
                    r = -a.real;
                    theta = atan2(0.0, -1.0);
                }
            } else {
                r = __Pyx_c_abs_double(a);
                theta = atan2(a.imag, a.real);
            }
            lnr = log(r);
            z_r = exp(lnr * b.real - theta * b.imag);
            z_theta = theta * b.real + lnr * b.imag;
            z.real = z_r * cos(z_theta);
            z.imag = z_r * sin(z_theta);
            return z;
        }
    #endif
#endif

/* MemviewSliceCopyTemplate */
static __Pyx_memviewslice
__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
                                 const char *mode, int ndim,
                                 size_t sizeof_dtype, int contig_flag,
                                 int dtype_is_object)
{
    __Pyx_RefNannyDeclarations
    int i;
    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
    Py_buffer *buf = &from_memview->view;
    PyObject *shape_tuple = NULL;
    PyObject *temp_int = NULL;
    struct __pyx_array_obj *array_obj = NULL;
    struct __pyx_memoryview_obj *memview_obj = NULL;
    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
    for (i = 0; i < ndim; i++) {
        if (unlikely(from_mvs->suboffsets[i] >= 0)) {
            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
                                           "indirect dimensions (axis %d)", i);
            goto fail;
        }
    }
    shape_tuple = PyTuple_New(ndim);
    if (unlikely(!shape_tuple)) {
        goto fail;
    }
    __Pyx_GOTREF(shape_tuple);
    for(i = 0; i < ndim; i++) {
        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
        if(unlikely(!temp_int)) {
            goto fail;
        } else {
            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
            temp_int = NULL;
        }
    }
    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
    if (unlikely(!array_obj)) {
        goto fail;
    }
    __Pyx_GOTREF(array_obj);
    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
                                    (PyObject *) array_obj, contig_flag,
                                    dtype_is_object,
                                    from_mvs->memview->typeinfo);
    if (unlikely(!memview_obj))
        goto fail;
    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
        goto fail;
    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
                                                dtype_is_object) < 0))
        goto fail;
    goto no_fail;
fail:
    __Pyx_XDECREF(new_mvs.memview);
    new_mvs.memview = NULL;
    new_mvs.data = NULL;
no_fail:
    __Pyx_XDECREF(shape_tuple);
    __Pyx_XDECREF(temp_int);
    __Pyx_XDECREF(array_obj);
    __Pyx_RefNannyFinishContext();
    return new_mvs;
}

/* CIntFromPy */
static CYTHON_INLINE Py_intptr_t __Pyx_PyInt_As_Py_intptr_t(PyObject *x) {
    const Py_intptr_t neg_one = (Py_intptr_t) ((Py_intptr_t) 0 - (Py_intptr_t) 1), const_zero = (Py_intptr_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(Py_intptr_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (Py_intptr_t) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (Py_intptr_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(Py_intptr_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) >= 2 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((((Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(Py_intptr_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) >= 3 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((((((Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(Py_intptr_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) >= 4 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((((((((Py_intptr_t)digits[3]) << PyLong_SHIFT) | (Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (Py_intptr_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(Py_intptr_t, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(Py_intptr_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (Py_intptr_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(Py_intptr_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(Py_intptr_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 2 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((Py_intptr_t)-1)*(((((Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(Py_intptr_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 2 * PyLong_SHIFT) {
                            return (Py_intptr_t) ((((((Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(Py_intptr_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 3 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((Py_intptr_t)-1)*(((((((Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(Py_intptr_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 3 * PyLong_SHIFT) {
                            return (Py_intptr_t) ((((((((Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(Py_intptr_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 4 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((Py_intptr_t)-1)*(((((((((Py_intptr_t)digits[3]) << PyLong_SHIFT) | (Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(Py_intptr_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 4 * PyLong_SHIFT) {
                            return (Py_intptr_t) ((((((((((Py_intptr_t)digits[3]) << PyLong_SHIFT) | (Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(Py_intptr_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(Py_intptr_t, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(Py_intptr_t, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            Py_intptr_t val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (Py_intptr_t) -1;
        }
    } else {
        Py_intptr_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (Py_intptr_t) -1;
        val = __Pyx_PyInt_As_Py_intptr_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to Py_intptr_t");
    return (Py_intptr_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to Py_intptr_t");
    return (Py_intptr_t) -1;
}

/* CIntFromPy */
static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) {
    const size_t neg_one = (size_t) ((size_t) 0 - (size_t) 1), const_zero = (size_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(size_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(size_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (size_t) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (size_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(size_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) >= 2 * PyLong_SHIFT) {
                            return (size_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) >= 3 * PyLong_SHIFT) {
                            return (size_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) >= 4 * PyLong_SHIFT) {
                            return (size_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (size_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(size_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (size_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(size_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(size_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(size_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
                            return (size_t) (((size_t)-1)*(((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
                            return (size_t) ((((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
                            return (size_t) (((size_t)-1)*(((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
                            return (size_t) ((((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) {
                            return (size_t) (((size_t)-1)*(((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) {
                            return (size_t) ((((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(size_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            size_t val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (size_t) -1;
        }
    } else {
        size_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (size_t) -1;
        val = __Pyx_PyInt_As_size_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to size_t");
    return (size_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to size_t");
    return (size_t) -1;
}

/* CIntFromPy */
static CYTHON_INLINE int64_t __Pyx_PyInt_As_int64_t(PyObject *x) {
    const int64_t neg_one = (int64_t) ((int64_t) 0 - (int64_t) 1), const_zero = (int64_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(int64_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(int64_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (int64_t) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int64_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(int64_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(int64_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) >= 2 * PyLong_SHIFT) {
                            return (int64_t) (((((int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int64_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) >= 3 * PyLong_SHIFT) {
                            return (int64_t) (((((((int64_t)digits[2]) << PyLong_SHIFT) | (int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int64_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) >= 4 * PyLong_SHIFT) {
                            return (int64_t) (((((((((int64_t)digits[3]) << PyLong_SHIFT) | (int64_t)digits[2]) << PyLong_SHIFT) | (int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (int64_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(int64_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int64_t, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(int64_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int64_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int64_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(int64_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(int64_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(int64_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) - 1 > 2 * PyLong_SHIFT) {
                            return (int64_t) (((int64_t)-1)*(((((int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(int64_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) - 1 > 2 * PyLong_SHIFT) {
                            return (int64_t) ((((((int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(int64_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) - 1 > 3 * PyLong_SHIFT) {
                            return (int64_t) (((int64_t)-1)*(((((((int64_t)digits[2]) << PyLong_SHIFT) | (int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int64_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) - 1 > 3 * PyLong_SHIFT) {
                            return (int64_t) ((((((((int64_t)digits[2]) << PyLong_SHIFT) | (int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(int64_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) - 1 > 4 * PyLong_SHIFT) {
                            return (int64_t) (((int64_t)-1)*(((((((((int64_t)digits[3]) << PyLong_SHIFT) | (int64_t)digits[2]) << PyLong_SHIFT) | (int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int64_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int64_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int64_t) - 1 > 4 * PyLong_SHIFT) {
                            return (int64_t) ((((((((((int64_t)digits[3]) << PyLong_SHIFT) | (int64_t)digits[2]) << PyLong_SHIFT) | (int64_t)digits[1]) << PyLong_SHIFT) | (int64_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(int64_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int64_t, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(int64_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int64_t, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            int64_t val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (int64_t) -1;
        }
    } else {
        int64_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (int64_t) -1;
        val = __Pyx_PyInt_As_int64_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to int64_t");
    return (int64_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to int64_t");
    return (int64_t) -1;
}

/* CIntFromPy */
static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
    const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(int) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (int) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int) 0;
                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
                case 2:
                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (int) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(int) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int) 0;
                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(int) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            int val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (int) -1;
        }
    } else {
        int val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (int) -1;
        val = __Pyx_PyInt_As_int(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to int");
    return (int) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to int");
    return (int) -1;
}

/* CIntFromPy */
static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
    const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(long) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (long) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (long) 0;
                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
                case 2:
                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (long) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(long) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (long) 0;
                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(long) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            long val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (long) -1;
        }
    } else {
        long val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (long) -1;
        val = __Pyx_PyInt_As_long(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to long");
    return (long) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to long");
    return (long) -1;
}

/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
    const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(int) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(int) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(int),
                                     little, !is_unsigned);
    }
}

/* CIntFromPy */
static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
    const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(char) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (char) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (char) 0;
                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])
                case 2:
                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {
                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {
                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {
                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (char) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(char) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (char) 0;
                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(char) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            char val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (char) -1;
        }
    } else {
        char val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (char) -1;
        val = __Pyx_PyInt_As_char(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to char");
    return (char) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to char");
    return (char) -1;
}

/* IsLittleEndian */
static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
{
  union {
    uint32_t u32;
    uint8_t u8[4];
  } S;
  S.u32 = 0x01020304;
  return S.u8[0] == 4;
}

/* BufferFormatCheck */
static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
                              __Pyx_BufFmt_StackElem* stack,
                              __Pyx_TypeInfo* type) {
  stack[0].field = &ctx->root;
  stack[0].parent_offset = 0;
  ctx->root.type = type;
  ctx->root.name = "buffer dtype";
  ctx->root.offset = 0;
  ctx->head = stack;
  ctx->head->field = &ctx->root;
  ctx->fmt_offset = 0;
  ctx->head->parent_offset = 0;
  ctx->new_packmode = '@';
  ctx->enc_packmode = '@';
  ctx->new_count = 1;
  ctx->enc_count = 0;
  ctx->enc_type = 0;
  ctx->is_complex = 0;
  ctx->is_valid_array = 0;
  ctx->struct_alignment = 0;
  while (type->typegroup == 'S') {
    ++ctx->head;
    ctx->head->field = type->fields;
    ctx->head->parent_offset = 0;
    type = type->fields->type;
  }
}
static int __Pyx_BufFmt_ParseNumber(const char** ts) {
    int count;
    const char* t = *ts;
    if (*t < '0' || *t > '9') {
      return -1;
    } else {
        count = *t++ - '0';
        while (*t >= '0' && *t <= '9') {
            count *= 10;
            count += *t++ - '0';
        }
    }
    *ts = t;
    return count;
}
static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
    int number = __Pyx_BufFmt_ParseNumber(ts);
    if (number == -1)
        PyErr_Format(PyExc_ValueError,\
                     "Does not understand character buffer dtype format string ('%c')", **ts);
    return number;
}
static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
  PyErr_Format(PyExc_ValueError,
               "Unexpected format string character: '%c'", ch);
}
static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
  switch (ch) {
    case '?': return "'bool'";
    case 'c': return "'char'";
    case 'b': return "'signed char'";
    case 'B': return "'unsigned char'";
    case 'h': return "'short'";
    case 'H': return "'unsigned short'";
    case 'i': return "'int'";
    case 'I': return "'unsigned int'";
    case 'l': return "'long'";
    case 'L': return "'unsigned long'";
    case 'q': return "'long long'";
    case 'Q': return "'unsigned long long'";
    case 'f': return (is_complex ? "'complex float'" : "'float'");
    case 'd': return (is_complex ? "'complex double'" : "'double'");
    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
    case 'T': return "a struct";
    case 'O': return "Python object";
    case 'P': return "a pointer";
    case 's': case 'p': return "a string";
    case 0: return "end";
    default: return "unparseable format string";
  }
}
static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
  switch (ch) {
    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
    case 'h': case 'H': return 2;
    case 'i': case 'I': case 'l': case 'L': return 4;
    case 'q': case 'Q': return 8;
    case 'f': return (is_complex ? 8 : 4);
    case 'd': return (is_complex ? 16 : 8);
    case 'g': {
      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
      return 0;
    }
    case 'O': case 'P': return sizeof(void*);
    default:
      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
      return 0;
    }
}
static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
  switch (ch) {
    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
    case 'h': case 'H': return sizeof(short);
    case 'i': case 'I': return sizeof(int);
    case 'l': case 'L': return sizeof(long);
    #ifdef HAVE_LONG_LONG
    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
    #endif
    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
    case 'O': case 'P': return sizeof(void*);
    default: {
      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
      return 0;
    }
  }
}
typedef struct { char c; short x; } __Pyx_st_short;
typedef struct { char c; int x; } __Pyx_st_int;
typedef struct { char c; long x; } __Pyx_st_long;
typedef struct { char c; float x; } __Pyx_st_float;
typedef struct { char c; double x; } __Pyx_st_double;
typedef struct { char c; long double x; } __Pyx_st_longdouble;
typedef struct { char c; void *x; } __Pyx_st_void_p;
#ifdef HAVE_LONG_LONG
typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
#endif
static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
  switch (ch) {
    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
#ifdef HAVE_LONG_LONG
    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
#endif
    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
    default:
      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
      return 0;
    }
}
/* These are for computing the padding at the end of the struct to align
   on the first member of the struct. This will probably the same as above,
   but we don't have any guarantees.
 */
typedef struct { short x; char c; } __Pyx_pad_short;
typedef struct { int x; char c; } __Pyx_pad_int;
typedef struct { long x; char c; } __Pyx_pad_long;
typedef struct { float x; char c; } __Pyx_pad_float;
typedef struct { double x; char c; } __Pyx_pad_double;
typedef struct { long double x; char c; } __Pyx_pad_longdouble;
typedef struct { void *x; char c; } __Pyx_pad_void_p;
#ifdef HAVE_LONG_LONG
typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
#endif
static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
  switch (ch) {
    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
#ifdef HAVE_LONG_LONG
    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
#endif
    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
    default:
      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
      return 0;
    }
}
static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
  switch (ch) {
    case 'c':
        return 'H';
    case 'b': case 'h': case 'i':
    case 'l': case 'q': case 's': case 'p':
        return 'I';
    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
        return 'U';
    case 'f': case 'd': case 'g':
        return (is_complex ? 'C' : 'R');
    case 'O':
        return 'O';
    case 'P':
        return 'P';
    default: {
      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
      return 0;
    }
  }
}
static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
    const char* expected;
    const char* quote;
    if (ctx->head == NULL) {
      expected = "end";
      quote = "";
    } else {
      expected = ctx->head->field->type->name;
      quote = "'";
    }
    PyErr_Format(PyExc_ValueError,
                 "Buffer dtype mismatch, expected %s%s%s but got %s",
                 quote, expected, quote,
                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
  } else {
    __Pyx_StructField* field = ctx->head->field;
    __Pyx_StructField* parent = (ctx->head - 1)->field;
    PyErr_Format(PyExc_ValueError,
                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
                 parent->type->name, field->name);
  }
}
static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
  char group;
  size_t size, offset, arraysize = 1;
  if (ctx->enc_type == 0) return 0;
  if (ctx->head->field->type->arraysize[0]) {
    int i, ndim = 0;
    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
        ndim = 1;
        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
            PyErr_Format(PyExc_ValueError,
                         "Expected a dimension of size %zu, got %zu",
                         ctx->head->field->type->arraysize[0], ctx->enc_count);
            return -1;
        }
    }
    if (!ctx->is_valid_array) {
      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
                   ctx->head->field->type->ndim, ndim);
      return -1;
    }
    for (i = 0; i < ctx->head->field->type->ndim; i++) {
      arraysize *= ctx->head->field->type->arraysize[i];
    }
    ctx->is_valid_array = 0;
    ctx->enc_count = 1;
  }
  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
  do {
    __Pyx_StructField* field = ctx->head->field;
    __Pyx_TypeInfo* type = field->type;
    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
    } else {
      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
    }
    if (ctx->enc_packmode == '@') {
      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
      size_t align_mod_offset;
      if (align_at == 0) return -1;
      align_mod_offset = ctx->fmt_offset % align_at;
      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
      if (ctx->struct_alignment == 0)
          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
                                                                 ctx->is_complex);
    }
    if (type->size != size || type->typegroup != group) {
      if (type->typegroup == 'C' && type->fields != NULL) {
        size_t parent_offset = ctx->head->parent_offset + field->offset;
        ++ctx->head;
        ctx->head->field = type->fields;
        ctx->head->parent_offset = parent_offset;
        continue;
      }
      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
      } else {
          __Pyx_BufFmt_RaiseExpected(ctx);
          return -1;
      }
    }
    offset = ctx->head->parent_offset + field->offset;
    if (ctx->fmt_offset != offset) {
      PyErr_Format(PyExc_ValueError,
                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
      return -1;
    }
    ctx->fmt_offset += size;
    if (arraysize)
      ctx->fmt_offset += (arraysize - 1) * size;
    --ctx->enc_count;
    while (1) {
      if (field == &ctx->root) {
        ctx->head = NULL;
        if (ctx->enc_count != 0) {
          __Pyx_BufFmt_RaiseExpected(ctx);
          return -1;
        }
        break;
      }
      ctx->head->field = ++field;
      if (field->type == NULL) {
        --ctx->head;
        field = ctx->head->field;
        continue;
      } else if (field->type->typegroup == 'S') {
        size_t parent_offset = ctx->head->parent_offset + field->offset;
        if (field->type->fields->type == NULL) continue;
        field = field->type->fields;
        ++ctx->head;
        ctx->head->field = field;
        ctx->head->parent_offset = parent_offset;
        break;
      } else {
        break;
      }
    }
  } while (ctx->enc_count);
  ctx->enc_type = 0;
  ctx->is_complex = 0;
  return 0;
}
static PyObject *
__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
{
    const char *ts = *tsp;
    int i = 0, number, ndim;
    ++ts;
    if (ctx->new_count != 1) {
        PyErr_SetString(PyExc_ValueError,
                        "Cannot handle repeated arrays in format string");
        return NULL;
    }
    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
    ndim = ctx->head->field->type->ndim;
    while (*ts && *ts != ')') {
        switch (*ts) {
            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
            default:  break;
        }
        number = __Pyx_BufFmt_ExpectNumber(&ts);
        if (number == -1) return NULL;
        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
            return PyErr_Format(PyExc_ValueError,
                        "Expected a dimension of size %zu, got %d",
                        ctx->head->field->type->arraysize[i], number);
        if (*ts != ',' && *ts != ')')
            return PyErr_Format(PyExc_ValueError,
                                "Expected a comma in format string, got '%c'", *ts);
        if (*ts == ',') ts++;
        i++;
    }
    if (i != ndim)
        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
                            ctx->head->field->type->ndim, i);
    if (!*ts) {
        PyErr_SetString(PyExc_ValueError,
                        "Unexpected end of format string, expected ')'");
        return NULL;
    }
    ctx->is_valid_array = 1;
    ctx->new_count = 1;
    *tsp = ++ts;
    return Py_None;
}
static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
  int got_Z = 0;
  while (1) {
    switch(*ts) {
      case 0:
        if (ctx->enc_type != 0 && ctx->head == NULL) {
          __Pyx_BufFmt_RaiseExpected(ctx);
          return NULL;
        }
        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
        if (ctx->head != NULL) {
          __Pyx_BufFmt_RaiseExpected(ctx);
          return NULL;
        }
        return ts;
      case ' ':
      case '\r':
      case '\n':
        ++ts;
        break;
      case '<':
        if (!__Pyx_Is_Little_Endian()) {
          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
          return NULL;
        }
        ctx->new_packmode = '=';
        ++ts;
        break;
      case '>':
      case '!':
        if (__Pyx_Is_Little_Endian()) {
          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
          return NULL;
        }
        ctx->new_packmode = '=';
        ++ts;
        break;
      case '=':
      case '@':
      case '^':
        ctx->new_packmode = *ts++;
        break;
      case 'T':
        {
          const char* ts_after_sub;
          size_t i, struct_count = ctx->new_count;
          size_t struct_alignment = ctx->struct_alignment;
          ctx->new_count = 1;
          ++ts;
          if (*ts != '{') {
            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
            return NULL;
          }
          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
          ctx->enc_type = 0;
          ctx->enc_count = 0;
          ctx->struct_alignment = 0;
          ++ts;
          ts_after_sub = ts;
          for (i = 0; i != struct_count; ++i) {
            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
            if (!ts_after_sub) return NULL;
          }
          ts = ts_after_sub;
          if (struct_alignment) ctx->struct_alignment = struct_alignment;
        }
        break;
      case '}':
        {
          size_t alignment = ctx->struct_alignment;
          ++ts;
          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
          ctx->enc_type = 0;
          if (alignment && ctx->fmt_offset % alignment) {
            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
          }
        }
        return ts;
      case 'x':
        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
        ctx->fmt_offset += ctx->new_count;
        ctx->new_count = 1;
        ctx->enc_count = 0;
        ctx->enc_type = 0;
        ctx->enc_packmode = ctx->new_packmode;
        ++ts;
        break;
      case 'Z':
        got_Z = 1;
        ++ts;
        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
          return NULL;
        }
        CYTHON_FALLTHROUGH;
      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
      case 'l': case 'L': case 'q': case 'Q':
      case 'f': case 'd': case 'g':
      case 'O': case 'p':
        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
          ctx->enc_count += ctx->new_count;
          ctx->new_count = 1;
          got_Z = 0;
          ++ts;
          break;
        }
        CYTHON_FALLTHROUGH;
      case 's':
        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
        ctx->enc_count = ctx->new_count;
        ctx->enc_packmode = ctx->new_packmode;
        ctx->enc_type = *ts;
        ctx->is_complex = got_Z;
        ++ts;
        ctx->new_count = 1;
        got_Z = 0;
        break;
      case ':':
        ++ts;
        while(*ts != ':') ++ts;
        ++ts;
        break;
      case '(':
        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
        break;
      default:
        {
          int number = __Pyx_BufFmt_ExpectNumber(&ts);
          if (number == -1) return NULL;
          ctx->new_count = (size_t)number;
        }
    }
  }
}

/* TypeInfoCompare */
  static int
__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
{
    int i;
    if (!a || !b)
        return 0;
    if (a == b)
        return 1;
    if (a->size != b->size || a->typegroup != b->typegroup ||
            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
        if (a->typegroup == 'H' || b->typegroup == 'H') {
            return a->size == b->size;
        } else {
            return 0;
        }
    }
    if (a->ndim) {
        for (i = 0; i < a->ndim; i++)
            if (a->arraysize[i] != b->arraysize[i])
                return 0;
    }
    if (a->typegroup == 'S') {
        if (a->flags != b->flags)
            return 0;
        if (a->fields || b->fields) {
            if (!(a->fields && b->fields))
                return 0;
            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
                __Pyx_StructField *field_a = a->fields + i;
                __Pyx_StructField *field_b = b->fields + i;
                if (field_a->offset != field_b->offset ||
                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
                    return 0;
            }
            return !a->fields[i].type && !b->fields[i].type;
        }
    }
    return 1;
}

/* MemviewSliceValidateAndInit */
  static int
__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
{
    if (buf->shape[dim] <= 1)
        return 1;
    if (buf->strides) {
        if (spec & __Pyx_MEMVIEW_CONTIG) {
            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
                if (unlikely(buf->strides[dim] != sizeof(void *))) {
                    PyErr_Format(PyExc_ValueError,
                                 "Buffer is not indirectly contiguous "
                                 "in dimension %d.", dim);
                    goto fail;
                }
            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
                PyErr_SetString(PyExc_ValueError,
                                "Buffer and memoryview are not contiguous "
                                "in the same dimension.");
                goto fail;
            }
        }
        if (spec & __Pyx_MEMVIEW_FOLLOW) {
            Py_ssize_t stride = buf->strides[dim];
            if (stride < 0)
                stride = -stride;
            if (unlikely(stride < buf->itemsize)) {
                PyErr_SetString(PyExc_ValueError,
                                "Buffer and memoryview are not contiguous "
                                "in the same dimension.");
                goto fail;
            }
        }
    } else {
        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
            PyErr_Format(PyExc_ValueError,
                         "C-contiguous buffer is not contiguous in "
                         "dimension %d", dim);
            goto fail;
        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
            PyErr_Format(PyExc_ValueError,
                         "C-contiguous buffer is not indirect in "
                         "dimension %d", dim);
            goto fail;
        } else if (unlikely(buf->suboffsets)) {
            PyErr_SetString(PyExc_ValueError,
                            "Buffer exposes suboffsets but no strides");
            goto fail;
        }
    }
    return 1;
fail:
    return 0;
}
static int
__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)
{
    if (spec & __Pyx_MEMVIEW_DIRECT) {
        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
            PyErr_Format(PyExc_ValueError,
                         "Buffer not compatible with direct access "
                         "in dimension %d.", dim);
            goto fail;
        }
    }
    if (spec & __Pyx_MEMVIEW_PTR) {
        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
            PyErr_Format(PyExc_ValueError,
                         "Buffer is not indirectly accessible "
                         "in dimension %d.", dim);
            goto fail;
        }
    }
    return 1;
fail:
    return 0;
}
static int
__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
{
    int i;
    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
        Py_ssize_t stride = 1;
        for (i = 0; i < ndim; i++) {
            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
                PyErr_SetString(PyExc_ValueError,
                    "Buffer not fortran contiguous.");
                goto fail;
            }
            stride = stride * buf->shape[i];
        }
    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
        Py_ssize_t stride = 1;
        for (i = ndim - 1; i >- 1; i--) {
            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
                PyErr_SetString(PyExc_ValueError,
                    "Buffer not C contiguous.");
                goto fail;
            }
            stride = stride * buf->shape[i];
        }
    }
    return 1;
fail:
    return 0;
}
static int __Pyx_ValidateAndInit_memviewslice(
                int *axes_specs,
                int c_or_f_flag,
                int buf_flags,
                int ndim,
                __Pyx_TypeInfo *dtype,
                __Pyx_BufFmt_StackElem stack[],
                __Pyx_memviewslice *memviewslice,
                PyObject *original_obj)
{
    struct __pyx_memoryview_obj *memview, *new_memview;
    __Pyx_RefNannyDeclarations
    Py_buffer *buf;
    int i, spec = 0, retval = -1;
    __Pyx_BufFmt_Context ctx;
    int from_memoryview = __pyx_memoryview_check(original_obj);
    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
                                                            original_obj)->typeinfo)) {
        memview = (struct __pyx_memoryview_obj *) original_obj;
        new_memview = NULL;
    } else {
        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
                                            original_obj, buf_flags, 0, dtype);
        new_memview = memview;
        if (unlikely(!memview))
            goto fail;
    }
    buf = &memview->view;
    if (unlikely(buf->ndim != ndim)) {
        PyErr_Format(PyExc_ValueError,
                "Buffer has wrong number of dimensions (expected %d, got %d)",
                ndim, buf->ndim);
        goto fail;
    }
    if (new_memview) {
        __Pyx_BufFmt_Init(&ctx, stack, dtype);
        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
    }
    if (unlikely((unsigned) buf->itemsize != dtype->size)) {
        PyErr_Format(PyExc_ValueError,
                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
                     buf->itemsize,
                     (buf->itemsize > 1) ? "s" : "",
                     dtype->name,
                     dtype->size,
                     (dtype->size > 1) ? "s" : "");
        goto fail;
    }
    if (buf->len > 0) {
        for (i = 0; i < ndim; i++) {
            spec = axes_specs[i];
            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
                goto fail;
            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
                goto fail;
        }
        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
            goto fail;
    }
    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
                                         new_memview != NULL) == -1)) {
        goto fail;
    }
    retval = 0;
    goto no_fail;
fail:
    Py_XDECREF(new_memview);
    retval = -1;
no_fail:
    __Pyx_RefNannyFinishContext();
    return retval;
}

/* ObjectToMemviewSlice */
  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_nn_uint64_t(PyObject *obj, int writable_flag) {
    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
    __Pyx_BufFmt_StackElem stack[1];
    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
    int retcode;
    if (obj == Py_None) {
        result.memview = (struct __pyx_memoryview_obj *) Py_None;
        return result;
    }
    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1,
                                                 &__Pyx_TypeInfo_nn_uint64_t, stack,
                                                 &result, obj);
    if (unlikely(retcode == -1))
        goto __pyx_fail;
    return result;
__pyx_fail:
    result.memview = NULL;
    result.data = NULL;
    return result;
}

/* CheckBinaryVersion */
  static int __Pyx_check_binary_version(void) {
    char ctversion[4], rtversion[4];
    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
        char message[200];
        PyOS_snprintf(message, sizeof(message),
                      "compiletime version %s of module '%.100s' "
                      "does not match runtime version %s",
                      ctversion, __Pyx_MODULE_NAME, rtversion);
        return PyErr_WarnEx(NULL, message, 1);
    }
    return 0;
}

/* VoidPtrImport */
  #ifndef __PYX_HAVE_RT_ImportVoidPtr
#define __PYX_HAVE_RT_ImportVoidPtr
static int __Pyx_ImportVoidPtr(PyObject *module, const char *name, void **p, const char *sig) {
    PyObject *d = 0;
    PyObject *cobj = 0;
    d = PyObject_GetAttrString(module, (char *)"__pyx_capi__");
    if (!d)
        goto bad;
    cobj = PyDict_GetItemString(d, name);
    if (!cobj) {
        PyErr_Format(PyExc_ImportError,
            "%.200s does not export expected C variable %.200s",
                PyModule_GetName(module), name);
        goto bad;
    }
#if PY_VERSION_HEX >= 0x02070000
    if (!PyCapsule_IsValid(cobj, sig)) {
        PyErr_Format(PyExc_TypeError,
            "C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
             PyModule_GetName(module), name, sig, PyCapsule_GetName(cobj));
        goto bad;
    }
    *p = PyCapsule_GetPointer(cobj, sig);
#else
    {const char *desc, *s1, *s2;
    desc = (const char *)PyCObject_GetDesc(cobj);
    if (!desc)
        goto bad;
    s1 = desc; s2 = sig;
    while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; }
    if (*s1 != *s2) {
        PyErr_Format(PyExc_TypeError,
            "C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
             PyModule_GetName(module), name, sig, desc);
        goto bad;
    }
    *p = PyCObject_AsVoidPtr(cobj);}
#endif
    if (!(*p))
        goto bad;
    Py_DECREF(d);
    return 0;
bad:
    Py_XDECREF(d);
    return -1;
}
#endif

/* FunctionImport */
  #ifndef __PYX_HAVE_RT_ImportFunction
#define __PYX_HAVE_RT_ImportFunction
static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig) {
    PyObject *d = 0;
    PyObject *cobj = 0;
    union {
        void (*fp)(void);
        void *p;
    } tmp;
    d = PyObject_GetAttrString(module, (char *)"__pyx_capi__");
    if (!d)
        goto bad;
    cobj = PyDict_GetItemString(d, funcname);
    if (!cobj) {
        PyErr_Format(PyExc_ImportError,
            "%.200s does not export expected C function %.200s",
                PyModule_GetName(module), funcname);
        goto bad;
    }
#if PY_VERSION_HEX >= 0x02070000
    if (!PyCapsule_IsValid(cobj, sig)) {
        PyErr_Format(PyExc_TypeError,
            "C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
             PyModule_GetName(module), funcname, sig, PyCapsule_GetName(cobj));
        goto bad;
    }
    tmp.p = PyCapsule_GetPointer(cobj, sig);
#else
    {const char *desc, *s1, *s2;
    desc = (const char *)PyCObject_GetDesc(cobj);
    if (!desc)
        goto bad;
    s1 = desc; s2 = sig;
    while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; }
    if (*s1 != *s2) {
        PyErr_Format(PyExc_TypeError,
            "C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
             PyModule_GetName(module), funcname, sig, desc);
        goto bad;
    }
    tmp.p = PyCObject_AsVoidPtr(cobj);}
#endif
    *f = tmp.fp;
    if (!(*f))
        goto bad;
    Py_DECREF(d);
    return 0;
bad:
    Py_XDECREF(d);
    return -1;
}
#endif

/* InitStrings */
  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
    while (t->p) {
        #if PY_MAJOR_VERSION < 3
        if (t->is_unicode) {
            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
        } else if (t->intern) {
            *t->p = PyString_InternFromString(t->s);
        } else {
            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
        }
        #else
        if (t->is_unicode | t->is_str) {
            if (t->intern) {
                *t->p = PyUnicode_InternFromString(t->s);
            } else if (t->encoding) {
                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
            } else {
                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
            }
        } else {
            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
        }
        #endif
        if (!*t->p)
            return -1;
        if (PyObject_Hash(*t->p) == -1)
            return -1;
        ++t;
    }
    return 0;
}

static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
}
static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
    Py_ssize_t ignore;
    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
}
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
#if !CYTHON_PEP393_ENABLED
static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
    char* defenc_c;
    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
    if (!defenc) return NULL;
    defenc_c = PyBytes_AS_STRING(defenc);
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
    {
        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
        char* c;
        for (c = defenc_c; c < end; c++) {
            if ((unsigned char) (*c) >= 128) {
                PyUnicode_AsASCIIString(o);
                return NULL;
            }
        }
    }
#endif
    *length = PyBytes_GET_SIZE(defenc);
    return defenc_c;
}
#else
static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
    if (likely(PyUnicode_IS_ASCII(o))) {
        *length = PyUnicode_GET_LENGTH(o);
        return PyUnicode_AsUTF8(o);
    } else {
        PyUnicode_AsASCIIString(o);
        return NULL;
    }
#else
    return PyUnicode_AsUTF8AndSize(o, length);
#endif
}
#endif
#endif
static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
    if (
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
            __Pyx_sys_getdefaultencoding_not_ascii &&
#endif
            PyUnicode_Check(o)) {
        return __Pyx_PyUnicode_AsStringAndSize(o, length);
    } else
#endif
#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
    if (PyByteArray_Check(o)) {
        *length = PyByteArray_GET_SIZE(o);
        return PyByteArray_AS_STRING(o);
    } else
#endif
    {
        char* result;
        int r = PyBytes_AsStringAndSize(o, &result, length);
        if (unlikely(r < 0)) {
            return NULL;
        } else {
            return result;
        }
    }
}
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
   int is_true = x == Py_True;
   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
   else return PyObject_IsTrue(x);
}
static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
    int retval;
    if (unlikely(!x)) return -1;
    retval = __Pyx_PyObject_IsTrue(x);
    Py_DECREF(x);
    return retval;
}
static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
#if PY_MAJOR_VERSION >= 3
    if (PyLong_Check(result)) {
        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
                "__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.",
                Py_TYPE(result)->tp_name)) {
            Py_DECREF(result);
            return NULL;
        }
        return result;
    }
#endif
    PyErr_Format(PyExc_TypeError,
                 "__%.4s__ returned non-%.4s (type %.200s)",
                 type_name, type_name, Py_TYPE(result)->tp_name);
    Py_DECREF(result);
    return NULL;
}
static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
#if CYTHON_USE_TYPE_SLOTS
  PyNumberMethods *m;
#endif
  const char *name = NULL;
  PyObject *res = NULL;
#if PY_MAJOR_VERSION < 3
  if (likely(PyInt_Check(x) || PyLong_Check(x)))
#else
  if (likely(PyLong_Check(x)))
#endif
    return __Pyx_NewRef(x);
#if CYTHON_USE_TYPE_SLOTS
  m = Py_TYPE(x)->tp_as_number;
  #if PY_MAJOR_VERSION < 3
  if (m && m->nb_int) {
    name = "int";
    res = m->nb_int(x);
  }
  else if (m && m->nb_long) {
    name = "long";
    res = m->nb_long(x);
  }
  #else
  if (likely(m && m->nb_int)) {
    name = "int";
    res = m->nb_int(x);
  }
  #endif
#else
  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
    res = PyNumber_Int(x);
  }
#endif
  if (likely(res)) {
#if PY_MAJOR_VERSION < 3
    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
#else
    if (unlikely(!PyLong_CheckExact(res))) {
#endif
        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
    }
  }
  else if (!PyErr_Occurred()) {
    PyErr_SetString(PyExc_TypeError,
                    "an integer is required");
  }
  return res;
}
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
  Py_ssize_t ival;
  PyObject *x;
#if PY_MAJOR_VERSION < 3
  if (likely(PyInt_CheckExact(b))) {
    if (sizeof(Py_ssize_t) >= sizeof(long))
        return PyInt_AS_LONG(b);
    else
        return PyInt_AsSsize_t(b);
  }
#endif
  if (likely(PyLong_CheckExact(b))) {
    #if CYTHON_USE_PYLONG_INTERNALS
    const digit* digits = ((PyLongObject*)b)->ob_digit;
    const Py_ssize_t size = Py_SIZE(b);
    if (likely(__Pyx_sst_abs(size) <= 1)) {
        ival = likely(size) ? digits[0] : 0;
        if (size == -1) ival = -ival;
        return ival;
    } else {
      switch (size) {
         case 2:
           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -2:
           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case 3:
           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -3:
           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case 4:
           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -4:
           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
      }
    }
    #endif
    return PyLong_AsSsize_t(b);
  }
  x = PyNumber_Index(b);
  if (!x) return -1;
  ival = PyInt_AsSsize_t(x);
  Py_DECREF(x);
  return ival;
}
static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
}
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
    return PyInt_FromSize_t(ival);
}


#endif /* Py_PYTHON_H */
