/* Generated by Cython 0.24 */

/* BEGIN: Cython Metadata
{
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            [
                "RS_MLFG_1279_861",
                "1"
            ]
        ],
        "depends": [
            "/home/kevin/anaconda/lib/python3.5/site-packages/numpy/core/include/numpy/arrayobject.h",
            "/home/kevin/anaconda/lib/python3.5/site-packages/numpy/core/include/numpy/ufuncobject.h",
            "randomstate/aligned_malloc.h",
            "randomstate/distributions.h"
        ],
        "extra_compile_args": [
            "-std=c99",
            "-msse2"
        ],
        "include_dirs": [
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            "/home/kevin/anaconda/lib/python3.5/site-packages/numpy/core/include",
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        ]
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}
END: Cython Metadata */

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#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
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typedef struct {
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#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)

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  #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 defined(WIN32) || defined(MS_WINDOWS)
  #define _USE_MATH_DEFINES
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#include <math.h>
#ifdef NAN
#define __PYX_NAN() ((float) NAN)
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static CYTHON_INLINE float __PYX_NAN() {
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  memset(&value, 0xFF, sizeof(value));
  return value;
}
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#define __PYX_ERR(f_index, lineno, Ln_error) \
{ \
  __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \
}

#if PY_MAJOR_VERSION >= 3
  #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)
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#ifndef __PYX_EXTERN_C
  #ifdef __cplusplus
    #define __PYX_EXTERN_C extern "C"
  #else
    #define __PYX_EXTERN_C extern
  #endif
#endif

#define __PYX_HAVE__randomstate__mlfg_1279_861
#define __PYX_HAVE_API__randomstate__mlfg_1279_861
#include "string.h"
#include "stdio.h"
#include "stdlib.h"
#include "numpy/arrayobject.h"
#include "numpy/ufuncobject.h"
#include "stdint.h"
#include "distributions.h"
#include "aligned_malloc.h"
#include "pythread.h"
#include "pystate.h"
#ifdef _OPENMP
#include <omp.h>
#endif /* _OPENMP */

#ifdef PYREX_WITHOUT_ASSERTIONS
#define CYTHON_WITHOUT_ASSERTIONS
#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_NCP_UNUSED
# if CYTHON_COMPILING_IN_CPYTHON
#  define CYTHON_NCP_UNUSED
# else
#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
# endif
#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_DEFAULT 0
#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)))  )
#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) && defined (_M_X64)
    #define __Pyx_sst_abs(value) _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 char* __Pyx_PyObject_AsString(PyObject*);
static CYTHON_INLINE 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_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsUString(s)    ((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)
#if PY_MAJOR_VERSION < 3
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);
}
#else
#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
#endif
#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)
#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
#if CYTHON_COMPILING_IN_CPYTHON
#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));
    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 PyObject *__pyx_m;
static PyObject *__pyx_d;
static PyObject *__pyx_b;
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;

/* None.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[] = {
  "randomstate/interface/mlfg-1279-861/mlfg-1279-861.pxi",
  "randomstate/array_utilities.pxi",
  "randomstate/mlfg_1279_861.pyx",
  "randomstate/array_fillers.pxi",
  "randomstate/bounded_integers.pxi",
  "__init__.pxd",
  "stringsource",
  "type.pxd",
  "bool.pxd",
  "complex.pxd",
};
/* 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;

/* 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;

/* 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


/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":725
 * # 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":726
 * 
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":727
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":728
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":732
 * #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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":733
 * 
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":734
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":735
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":739
 * #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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":740
 * 
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":749
 * # 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":750
 * # 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":751
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":753
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":754
 * 
 * ctypedef npy_ulong      uint_t
 * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
 * ctypedef npy_ulonglong  ulonglong_t
 * 
 */
typedef npy_ulonglong __pyx_t_5numpy_ulong_t;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":755
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":757
 * ctypedef npy_ulonglong  ulonglong_t
 * 
 * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uintp      uintp_t
 * 
 */
typedef npy_intp __pyx_t_5numpy_intp_t;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":758
 * 
 * ctypedef npy_intp       intp_t
 * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_double     float_t
 */
typedef npy_uintp __pyx_t_5numpy_uintp_t;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":760
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":761
 * 
 * ctypedef npy_double     float_t
 * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
 * ctypedef npy_longdouble longdouble_t
 * 
 */
typedef npy_double __pyx_t_5numpy_double_t;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":762
 * 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;

/* "randomstate/interface/mlfg-1279-861/mlfg-1279-861.pxi":28
 *     cdef void set_seed_by_array(aug_state* state, uint64_t *seed, int count)
 * 
 * ctypedef uint64_t rng_state_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef mlfg_state rng_t
 */
typedef uint64_t __pyx_t_11randomstate_13mlfg_1279_861_rng_state_t;

/* "randomstate/bounded_integers.pxi":12
 *                  }
 * 
 * ctypedef np.npy_bool bool_t             # <<<<<<<<<<<<<<
 * 
 * 
 */
typedef npy_bool __pyx_t_11randomstate_13mlfg_1279_861_bool_t;
/* None.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

/* None.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


/*--- Type declarations ---*/
struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState;
struct __pyx_array_obj;
struct __pyx_MemviewEnum_obj;
struct __pyx_memoryview_obj;
struct __pyx_memoryviewslice_obj;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":764
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":765
 * 
 * ctypedef npy_cfloat      cfloat_t
 * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
 * ctypedef npy_clongdouble clongdouble_t
 * 
 */
typedef npy_cdouble __pyx_t_5numpy_cdouble_t;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":766
 * 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;

/* "../../anaconda/lib/python3.5/site-packages/Cython/Includes/numpy/__init__.pxd":768
 * ctypedef npy_clongdouble clongdouble_t
 * 
 * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
 * 
 * cdef inline object PyArray_MultiIterNew1(a):
 */
typedef npy_cdouble __pyx_t_5numpy_complex_t;

/* "randomstate/array_utilities.pxi":32
 * cdef double POISSON_LAM_MAX = <double>np.iinfo('l').max - np.sqrt(np.iinfo('l').max)*10
 * 
 * cdef enum ConstraintType:             # <<<<<<<<<<<<<<
 *     CONS_NONE
 *     CONS_NON_NEGATIVE
 */
enum __pyx_t_11randomstate_13mlfg_1279_861_ConstraintType {
  __pyx_e_11randomstate_13mlfg_1279_861_CONS_NONE,
  __pyx_e_11randomstate_13mlfg_1279_861_CONS_NON_NEGATIVE,
  __pyx_e_11randomstate_13mlfg_1279_861_CONS_POSITIVE,
  __pyx_e_11randomstate_13mlfg_1279_861_CONS_BOUNDED_0_1,
  __pyx_e_11randomstate_13mlfg_1279_861_CONS_BOUNDED_0_1_NOTNAN,
  __pyx_e_11randomstate_13mlfg_1279_861_CONS_GT_1,
  __pyx_e_11randomstate_13mlfg_1279_861_CONS_GTE_1,
  __pyx_e_11randomstate_13mlfg_1279_861_CONS_POISSON
};

/* "randomstate/interface/mlfg-1279-861/mlfg-1279-861.pxi":30
 * ctypedef uint64_t rng_state_t
 * 
 * ctypedef mlfg_state rng_t             # <<<<<<<<<<<<<<
 * 
 * cdef object _get_state(aug_state state):
 */
typedef mlfg_state __pyx_t_11randomstate_13mlfg_1279_861_rng_t;

/* "randomstate/array_utilities.pxi":1
 * ctypedef double (* random_double_0)(aug_state* state) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (* random_double_1)(aug_state* state, double a) nogil
 * ctypedef double (* random_double_2)(aug_state* state, double a, double b) nogil
 */
typedef double (*__pyx_t_11randomstate_13mlfg_1279_861_random_double_0)(aug_state *);

/* "randomstate/array_utilities.pxi":2
 * ctypedef double (* random_double_0)(aug_state* state) nogil
 * ctypedef double (* random_double_1)(aug_state* state, double a) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (* random_double_2)(aug_state* state, double a, double b) nogil
 * ctypedef double (* random_double_3)(aug_state* state, double a, double b, double c) nogil
 */
typedef double (*__pyx_t_11randomstate_13mlfg_1279_861_random_double_1)(aug_state *, double);

/* "randomstate/array_utilities.pxi":3
 * ctypedef double (* random_double_0)(aug_state* state) nogil
 * ctypedef double (* random_double_1)(aug_state* state, double a) nogil
 * ctypedef double (* random_double_2)(aug_state* state, double a, double b) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (* random_double_3)(aug_state* state, double a, double b, double c) nogil
 * 
 */
typedef double (*__pyx_t_11randomstate_13mlfg_1279_861_random_double_2)(aug_state *, double, double);

/* "randomstate/array_utilities.pxi":4
 * ctypedef double (* random_double_1)(aug_state* state, double a) nogil
 * ctypedef double (* random_double_2)(aug_state* state, double a, double b) nogil
 * ctypedef double (* random_double_3)(aug_state* state, double a, double b, double c) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef float (* random_float_1)(aug_state* state, float a) nogil
 */
typedef double (*__pyx_t_11randomstate_13mlfg_1279_861_random_double_3)(aug_state *, double, double, double);

/* "randomstate/array_utilities.pxi":6
 * ctypedef double (* random_double_3)(aug_state* state, double a, double b, double c) nogil
 * 
 * ctypedef float (* random_float_1)(aug_state* state, float a) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef long (* random_uint_0)(aug_state* state) nogil
 */
typedef float (*__pyx_t_11randomstate_13mlfg_1279_861_random_float_1)(aug_state *, float);

/* "randomstate/array_utilities.pxi":8
 * ctypedef float (* random_float_1)(aug_state* state, float a) nogil
 * 
 * ctypedef long (* random_uint_0)(aug_state* state) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* random_uint_d)(aug_state* state, double a) nogil
 * ctypedef long (* random_uint_dd)(aug_state* state, double a, double b) nogil
 */
typedef long (*__pyx_t_11randomstate_13mlfg_1279_861_random_uint_0)(aug_state *);

/* "randomstate/array_utilities.pxi":9
 * 
 * ctypedef long (* random_uint_0)(aug_state* state) nogil
 * ctypedef long (* random_uint_d)(aug_state* state, double a) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* random_uint_dd)(aug_state* state, double a, double b) nogil
 * ctypedef long (* random_uint_di)(aug_state* state, double a, uint64_t b) nogil
 */
typedef long (*__pyx_t_11randomstate_13mlfg_1279_861_random_uint_d)(aug_state *, double);

/* "randomstate/array_utilities.pxi":10
 * ctypedef long (* random_uint_0)(aug_state* state) nogil
 * ctypedef long (* random_uint_d)(aug_state* state, double a) nogil
 * ctypedef long (* random_uint_dd)(aug_state* state, double a, double b) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* random_uint_di)(aug_state* state, double a, uint64_t b) nogil
 * ctypedef long (* random_uint_i)(aug_state* state, long a) nogil
 */
typedef long (*__pyx_t_11randomstate_13mlfg_1279_861_random_uint_dd)(aug_state *, double, double);

/* "randomstate/array_utilities.pxi":11
 * ctypedef long (* random_uint_d)(aug_state* state, double a) nogil
 * ctypedef long (* random_uint_dd)(aug_state* state, double a, double b) nogil
 * ctypedef long (* random_uint_di)(aug_state* state, double a, uint64_t b) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* random_uint_i)(aug_state* state, long a) nogil
 * ctypedef long (* random_uint_iii)(aug_state* state, long a, long b, long c) nogil
 */
typedef long (*__pyx_t_11randomstate_13mlfg_1279_861_random_uint_di)(aug_state *, double, uint64_t);

/* "randomstate/array_utilities.pxi":12
 * ctypedef long (* random_uint_dd)(aug_state* state, double a, double b) nogil
 * ctypedef long (* random_uint_di)(aug_state* state, double a, uint64_t b) nogil
 * ctypedef long (* random_uint_i)(aug_state* state, long a) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* random_uint_iii)(aug_state* state, long a, long b, long c) nogil
 * 
 */
typedef long (*__pyx_t_11randomstate_13mlfg_1279_861_random_uint_i)(aug_state *, long);

/* "randomstate/array_utilities.pxi":13
 * ctypedef long (* random_uint_di)(aug_state* state, double a, uint64_t b) nogil
 * ctypedef long (* random_uint_i)(aug_state* state, long a) nogil
 * ctypedef long (* random_uint_iii)(aug_state* state, long a, long b, long c) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef uint32_t (* random_uint_0_32)(aug_state* state) nogil
 */
typedef long (*__pyx_t_11randomstate_13mlfg_1279_861_random_uint_iii)(aug_state *, long, long, long);

/* "randomstate/array_utilities.pxi":15
 * ctypedef long (* random_uint_iii)(aug_state* state, long a, long b, long c) nogil
 * 
 * ctypedef uint32_t (* random_uint_0_32)(aug_state* state) nogil             # <<<<<<<<<<<<<<
 * ctypedef uint32_t (* random_uint_1_i_32)(aug_state* state, uint32_t a) nogil
 * 
 */
typedef uint32_t (*__pyx_t_11randomstate_13mlfg_1279_861_random_uint_0_32)(aug_state *);

/* "randomstate/array_utilities.pxi":16
 * 
 * ctypedef uint32_t (* random_uint_0_32)(aug_state* state) nogil
 * ctypedef uint32_t (* random_uint_1_i_32)(aug_state* state, uint32_t a) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef int32_t (* random_int_2_i_32)(aug_state* state, int32_t a, int32_t b) nogil
 */
typedef uint32_t (*__pyx_t_11randomstate_13mlfg_1279_861_random_uint_1_i_32)(aug_state *, uint32_t);

/* "randomstate/array_utilities.pxi":18
 * ctypedef uint32_t (* random_uint_1_i_32)(aug_state* state, uint32_t a) nogil
 * 
 * ctypedef int32_t (* random_int_2_i_32)(aug_state* state, int32_t a, int32_t b) nogil             # <<<<<<<<<<<<<<
 * ctypedef int64_t (* random_int_2_i)(aug_state* state, int64_t a, int64_t b) nogil
 * 
 */
typedef int32_t (*__pyx_t_11randomstate_13mlfg_1279_861_random_int_2_i_32)(aug_state *, int32_t, int32_t);

/* "randomstate/array_utilities.pxi":19
 * 
 * ctypedef int32_t (* random_int_2_i_32)(aug_state* state, int32_t a, int32_t b) nogil
 * ctypedef int64_t (* random_int_2_i)(aug_state* state, int64_t a, int64_t b) nogil             # <<<<<<<<<<<<<<
 * 
 * cdef np.npy_intp compute_numel(size):
 */
typedef int64_t (*__pyx_t_11randomstate_13mlfg_1279_861_random_int_2_i)(aug_state *, int64_t, int64_t);

/* "randomstate/array_utilities.pxi":42
 *     CONS_POISSON
 * 
 * ctypedef ConstraintType constraint_type             # <<<<<<<<<<<<<<
 * 
 * 
 */
typedef enum __pyx_t_11randomstate_13mlfg_1279_861_ConstraintType __pyx_t_11randomstate_13mlfg_1279_861_constraint_type;

/* "randomstate/array_fillers.pxi":1
 * ctypedef void (* random_float_fill)(aug_state* state, np.npy_intp count, float *out) nogil             # <<<<<<<<<<<<<<
 * ctypedef void (* random_double_fill)(aug_state* state, np.npy_intp count, double *out) nogil
 * 
 */
typedef void (*__pyx_t_11randomstate_13mlfg_1279_861_random_float_fill)(aug_state *, npy_intp, float *);

/* "randomstate/array_fillers.pxi":2
 * ctypedef void (* random_float_fill)(aug_state* state, np.npy_intp count, float *out) nogil
 * ctypedef void (* random_double_fill)(aug_state* state, np.npy_intp count, double *out) nogil             # <<<<<<<<<<<<<<
 * 
 * 
 */
typedef void (*__pyx_t_11randomstate_13mlfg_1279_861_random_double_fill)(aug_state *, npy_intp, double *);

/* "randomstate/mlfg_1279_861.pyx":172
 * 
 * 
 * cdef class RandomState:             # <<<<<<<<<<<<<<
 *     CLASS_DOCSTRING
 * 
 */
struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState {
  PyObject_HEAD
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  PyObject *lock;
  PyObject *__pyx___seed;
  PyObject *__pyx___stream;
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/* "View.MemoryView":326
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struct __pyx_memoryview_obj {
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/* "randomstate/mlfg_1279_861.pyx":172
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/* "View.MemoryView":103
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/* "View.MemoryView":326
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struct __pyx_vtabstruct_memoryview {
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static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
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/* GetBuiltinName.proto */
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/* GetModuleGlobalName.proto */
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/* PyObjectCall.proto */
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static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
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/* PyObjectCallMethO.proto */
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static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
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/* PyObjectCallOneArg.proto */
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/* BufferFormatCheck.proto */
static CYTHON_INLINE int  __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj,
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static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
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/* MemviewSliceInit.proto */
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#define __Pyx_MEMVIEW_DIRECT   1
#define __Pyx_MEMVIEW_PTR      2
#define __Pyx_MEMVIEW_FULL     4
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#define __Pyx_MEMVIEW_STRIDED  16
#define __Pyx_MEMVIEW_FOLLOW   32
#define __Pyx_IS_C_CONTIG 1
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static int __Pyx_init_memviewslice(
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static CYTHON_INLINE int __pyx_add_acquisition_count_locked(
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static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(
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#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__)
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/* GetItemInt.proto */
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static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
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#include <string.h>

/* BytesEquals.proto */
static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);

/* UnicodeEquals.proto */
static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);

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

/* RaiseTooManyValuesToUnpack.proto */
static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);

/* RaiseNeedMoreValuesToUnpack.proto */
static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);

/* IterFinish.proto */
static CYTHON_INLINE int __Pyx_IterFinish(void);

/* UnpackItemEndCheck.proto */
static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);

/* PyIntBinop.proto */
#if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
#else
#define __Pyx_PyInt_SubtractObjC(op1, op2, intval, inplace)\
    (inplace ? PyNumber_InPlaceSubtract(op1, op2) : PyNumber_Subtract(op1, op2))
#endif

/* SliceObject.proto */
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, int wraparound);

/* SliceObject.proto */
#define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)\
    __Pyx_PyObject_SetSlice(obj, (PyObject*)NULL, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)
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, int wraparound);

/* PyObjectSetAttrStr.proto */
#if CYTHON_COMPILING_IN_CPYTHON
#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o,n,NULL)
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);
}
#else
#define __Pyx_PyObject_DelAttrStr(o,n)   PyObject_DelAttr(o,n)
#define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v)
#endif

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

/* Import.proto */
static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);

/* ImportFrom.proto */
static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);

/* ListAppend.proto */
#if CYTHON_COMPILING_IN_CPYTHON
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);
        Py_SIZE(list) = len+1;
        return 0;
    }
    return PyList_Append(list, x);
}
#else
#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
#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 CYTHON_INLINE 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);

/* DictGetItem.proto */
#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()) {
            PyObject* args = PyTuple_Pack(1, key);
            if (likely(args))
                PyErr_SetObject(PyExc_KeyError, args);
            Py_XDECREF(args);
        }
        return NULL;
    }
    Py_INCREF(value);
    return value;
}
#else
    #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
#endif

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

/* ArgTypeTest.proto */
static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
    const char *name, int exact);

/* 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*/
/* GetAttr.proto */
static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);

/* 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));

/* SwapException.proto */
#if CYTHON_COMPILING_IN_CPYTHON
#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

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_COMPILING_IN_CPYTHON
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);
        Py_SIZE(list) = len+1;
        return 0;
    }
    return PyList_Append(list, x);
}
#else
#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
#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
}

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

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

/* 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;

/* None.proto */
static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0};
static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1};

/* 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_uint64_t(uint64_t value);

/* MemviewDtypeToObject.proto */
static CYTHON_INLINE PyObject *__pyx_memview_get_nn_uint64_t(const char *itemp);
static CYTHON_INLINE int __pyx_memview_set_nn_uint64_t(const char *itemp, PyObject *obj);

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

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

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

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_long(npy_long value);

/* MemviewDtypeToObject.proto */
static CYTHON_INLINE PyObject *__pyx_memview_get_nn___pyx_t_5numpy_int_t(const char *itemp);
static CYTHON_INLINE int __pyx_memview_set_nn___pyx_t_5numpy_int_t(const char *itemp, PyObject *obj);

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

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int32_t(int32_t value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int16_t(int16_t value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int8_t(int8_t value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint32_t(uint32_t value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint16_t(uint16_t value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint8_t(uint8_t value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_bool(npy_bool value);

/* None.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

/* None.proto */
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);

/* None.proto */
#if CYTHON_CCOMPLEX
    #define __Pyx_c_eqf(a, b)   ((a)==(b))
    #define __Pyx_c_sumf(a, b)  ((a)+(b))
    #define __Pyx_c_difff(a, b) ((a)-(b))
    #define __Pyx_c_prodf(a, b) ((a)*(b))
    #define __Pyx_c_quotf(a, b) ((a)/(b))
    #define __Pyx_c_negf(a)     (-(a))
  #ifdef __cplusplus
    #define __Pyx_c_is_zerof(z) ((z)==(float)0)
    #define __Pyx_c_conjf(z)    (::std::conj(z))
    #if 1
        #define __Pyx_c_absf(z)     (::std::abs(z))
        #define __Pyx_c_powf(a, b)  (::std::pow(a, b))
    #endif
  #else
    #define __Pyx_c_is_zerof(z) ((z)==0)
    #define __Pyx_c_conjf(z)    (conjf(z))
    #if 1
        #define __Pyx_c_absf(z)     (cabsf(z))
        #define __Pyx_c_powf(a, b)  (cpowf(a, b))
    #endif
 #endif
#else
    static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex);
    static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex);
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex);
    #if 1
        static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex);
        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex);
    #endif
#endif

/* None.proto */
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);

/* None.proto */
#if CYTHON_CCOMPLEX
    #define __Pyx_c_eq(a, b)   ((a)==(b))
    #define __Pyx_c_sum(a, b)  ((a)+(b))
    #define __Pyx_c_diff(a, b) ((a)-(b))
    #define __Pyx_c_prod(a, b) ((a)*(b))
    #define __Pyx_c_quot(a, b) ((a)/(b))
    #define __Pyx_c_neg(a)     (-(a))
  #ifdef __cplusplus
    #define __Pyx_c_is_zero(z) ((z)==(double)0)
    #define __Pyx_c_conj(z)    (::std::conj(z))
    #if 1
        #define __Pyx_c_abs(z)     (::std::abs(z))
        #define __Pyx_c_pow(a, b)  (::std::pow(a, b))
    #endif
  #else
    #define __Pyx_c_is_zero(z) ((z)==0)
    #define __Pyx_c_conj(z)    (conj(z))
    #if 1
        #define __Pyx_c_abs(z)     (cabs(z))
        #define __Pyx_c_pow(a, b)  (cpow(a, b))
    #endif
 #endif
#else
    static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex);
    static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex);
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex);
    #if 1
        static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex);
        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex);
    #endif
#endif

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value);

/* 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 uint64_t __Pyx_PyInt_As_uint64_t(PyObject *);

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

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

/* CIntFromPy.proto */
static CYTHON_INLINE npy_long __Pyx_PyInt_As_npy_long(PyObject *);

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

/* CIntFromPy.proto */
static CYTHON_INLINE uint32_t __Pyx_PyInt_As_uint32_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE int32_t __Pyx_PyInt_As_int32_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE uint16_t __Pyx_PyInt_As_uint16_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE int16_t __Pyx_PyInt_As_int16_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE uint8_t __Pyx_PyInt_As_uint8_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE int8_t __Pyx_PyInt_As_int8_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_bool __Pyx_PyInt_As_npy_bool(PyObject *);

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

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

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

/* 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_ds_nn_uint64_t(PyObject *);

/* ObjectToMemviewSlice.proto */
static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_int_t(PyObject *);

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

/* PyIdentifierFromString.proto */
#if !defined(__Pyx_PyIdentifier_FromString)
#if PY_MAJOR_VERSION < 3
  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
#else
  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
#endif
#endif

/* ModuleImport.proto */
static PyObject *__Pyx_ImportModule(const char *name);

/* TypeImport.proto */
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);

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

static CYTHON_INLINE PyObject *__pyx_f_11randomstate_13mlfg_1279_861_11RandomState__shuffle_raw(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, npy_intp __pyx_v_n, npy_intp __pyx_v_itemsize, npy_intp __pyx_v_stride, char *__pyx_v_data, char *__pyx_v_buf); /* 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.buffer' */

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

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

/* Module declarations from '__builtin__' */

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

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

/* 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.bytes' */

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

/* Module declarations from 'cpython' */

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

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

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

/* 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_MultiIterNew2(PyObject *, PyObject *); /*proto*/
static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *, PyObject *, PyObject *); /*proto*/
static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/
static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/

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

/* Module declarations from 'cython' */

/* Module declarations from 'libc' */

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

/* Module declarations from 'randomstate.binomial' */

/* Module declarations from 'randomstate.cython_overrides' */

/* Module declarations from 'randomstate.mlfg_1279_861' */
static PyTypeObject *__pyx_ptype_11randomstate_13mlfg_1279_861_RandomState = 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 double __pyx_v_11randomstate_13mlfg_1279_861_POISSON_LAM_MAX;
static uint64_t __pyx_v_11randomstate_13mlfg_1279_861_MAXSIZE;
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 PyObject *__pyx_f_11randomstate_13mlfg_1279_861__get_state(aug_state); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__set_state(aug_state *, PyObject *); /*proto*/
static npy_intp __pyx_f_11randomstate_13mlfg_1279_861_compute_numel(PyObject *); /*proto*/
static int __pyx_f_11randomstate_13mlfg_1279_861_check_array_constraint(PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static int __pyx_f_11randomstate_13mlfg_1279_861_check_constraint(double, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_cont_broadcast_1(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_cont_broadcast_2(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_cont_broadcast_3(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_cont(aug_state *, void *, PyObject *, PyObject *, int, PyObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_d(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_dd(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_di(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_iii(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_i(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_disc(aug_state *, void *, PyObject *, PyObject *, int, int, PyObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type, PyObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_cont_broadcast_float_1(aug_state *, void *, PyObject *, PyObject *, PyArrayObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_cont_float(aug_state *, void *, PyObject *, PyObject *, PyObject *, PyObject *, __pyx_t_11randomstate_13mlfg_1279_861_constraint_type); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_double_fill(aug_state *, void *, PyObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861_float_fill(aug_state *, void *, PyObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_int64(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_int32(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_int16(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_int8(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_uint64(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_uint32(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_uint16(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_uint8(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__rand_bool(PyObject *, PyObject *, PyObject *, aug_state *, PyObject *); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__generate_seed(PyObject *); /*proto*/
static double __pyx_f_11randomstate_13mlfg_1279_861_kahan_sum(double *, npy_intp); /*proto*/
static PyObject *__pyx_f_11randomstate_13mlfg_1279_861__ensure_string(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 __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 };
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int_t = { "int_t", NULL, sizeof(__pyx_t_5numpy_int_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int_t), 0 };
#define __Pyx_MODULE_NAME "randomstate.mlfg_1279_861"
int __pyx_module_is_main_randomstate__mlfg_1279_861 = 0;

/* Implementation of 'randomstate.mlfg_1279_861' */
static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_ValueError;
static PyObject *__pyx_builtin_NotImplementedError;
static PyObject *__pyx_builtin_map;
static PyObject *__pyx_builtin_chr;
static PyObject *__pyx_builtin_TypeError;
static PyObject *__pyx_builtin_OverflowError;
static PyObject *__pyx_builtin_DeprecationWarning;
static PyObject *__pyx_builtin_RuntimeWarning;
static PyObject *__pyx_builtin_reversed;
static PyObject *__pyx_builtin_RuntimeError;
static PyObject *__pyx_builtin_MemoryError;
static PyObject *__pyx_builtin_enumerate;
static PyObject *__pyx_builtin_Ellipsis;
static PyObject *__pyx_builtin_id;
static PyObject *__pyx_builtin_IndexError;
static const char __pyx_k_0[] = " < 0";
static const char __pyx_k_1[] = " >= 1";
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_f[] = "f";
static const char __pyx_k_l[] = "l";
static const char __pyx_k_n[] = "n";
static const char __pyx_k_p[] = "p";
static const char __pyx_k_bm[] = "bm";
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_0_2[] = " <= 0";
static const char __pyx_k_1_2[] = " <= 1";
static const char __pyx_k_1_3[] = " < 1";
static const char __pyx_k__46[] = "";
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_chr[] = "chr";
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_int[] = "int";
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_map[] = "map";
static const char __pyx_k_max[] = "max";
static const char __pyx_k_obj[] = "obj";
static const char __pyx_k_sum[] = "sum";
static const char __pyx_k_svd[] = "svd";
static const char __pyx_k_sys[] = "sys";
static const char __pyx_k_zig[] = "zig";
static const char __pyx_k_0_or[] = " <= 0 or ";
static const char __pyx_k_Lock[] = "Lock";
static const char __pyx_k_axis[] = "axis";
static const char __pyx_k_base[] = "base";
static const char __pyx_k_beta[] = "beta";
static const char __pyx_k_bool[] = "bool_";
static const char __pyx_k_copy[] = "copy";
static const char __pyx_k_data[] = "data";
static const char __pyx_k_exit[] = "__exit__";
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_join[] = "join";
static const char __pyx_k_left[] = "left";
static const char __pyx_k_less[] = "less";
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_prng[] = "prng";
static const char __pyx_k_prod[] = "prod";
static const char __pyx_k_rand[] = "rand";
static const char __pyx_k_ranf[] = "ranf";
static const char __pyx_k_safe[] = "safe";
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_wald[] = "wald";
static const char __pyx_k_warn[] = "warn";
static const char __pyx_k_zipf[] = "zipf";
static const char __pyx_k_ASCII[] = "ASCII";
static const char __pyx_k_Not_a[] = "Not a ";
static const char __pyx_k_alpha[] = "alpha";
static const char __pyx_k_array[] = "array";
static const char __pyx_k_bytes[] = "bytes";
static const char __pyx_k_class[] = "__class__";
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_gamma[] = "gamma";
static const char __pyx_k_gauss[] = "gauss";
static const char __pyx_k_iinfo[] = "iinfo";
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_power[] = "power";
static const char __pyx_k_pvals[] = "pvals";
static const char __pyx_k_randn[] = "randn";
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_bool_2[] = "bool";
static const char __pyx_k_choice[] = "choice";
static const char __pyx_k_ctypes[] = "ctypes";
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_gumbel[] = "gumbel";
static const char __pyx_k_import[] = "__import__";
static const char __pyx_k_method[] = "method";
static const char __pyx_k_name_2[] = "__name__";
static const char __pyx_k_nbad_0[] = "nbad < 0";
static const char __pyx_k_normal[] = "normal";
static const char __pyx_k_object[] = "object";
static const char __pyx_k_pareto[] = "pareto";
static const char __pyx_k_rand_2[] = "_rand";
static const char __pyx_k_random[] = "random";
static const char __pyx_k_reduce[] = "reduce";
static const char __pyx_k_sample[] = "sample";
static const char __pyx_k_seed_0[] = "seed < 0";
static const char __pyx_k_stream[] = "stream";
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_unsafe[] = "unsafe";
static const char __pyx_k_MAXSIZE[] = "__MAXSIZE";
static const char __pyx_k_asarray[] = "asarray";
static const char __pyx_k_casting[] = "casting";
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_integer[] = "integer";
static const char __pyx_k_laplace[] = "laplace";
static const char __pyx_k_maxsize[] = "maxsize";
static const char __pyx_k_memview[] = "memview";
static const char __pyx_k_ngood_0[] = "ngood < 0";
static const char __pyx_k_nsample[] = "nsample";
static const char __pyx_k_poisson[] = "poisson";
static const char __pyx_k_randint[] = "randint";
static const char __pyx_k_replace[] = "replace";
static const char __pyx_k_reshape[] = "reshape";
static const char __pyx_k_shuffle[] = "shuffle";
static const char __pyx_k_signbit[] = "signbit";
static const char __pyx_k_squeeze[] = "squeeze";
static const char __pyx_k_strides[] = "strides";
static const char __pyx_k_tobytes[] = "tobytes";
static const char __pyx_k_uniform[] = "uniform";
static const char __pyx_k_version[] = "version";
static const char __pyx_k_weibull[] = "weibull";
static const char __pyx_k_Ellipsis[] = "Ellipsis";
static const char __pyx_k_binomial[] = "binomial";
static const char __pyx_k_fallback[] = "fallback";
static const char __pyx_k_floating[] = "floating";
static const char __pyx_k_isfinite[] = "isfinite";
static const char __pyx_k_itemsize[] = "itemsize";
static const char __pyx_k_logistic[] = "logistic";
static const char __pyx_k_low_high[] = "low >= high";
static const char __pyx_k_operator[] = "operator";
static const char __pyx_k_rayleigh[] = "rayleigh";
static const char __pyx_k_reversed[] = "reversed";
static const char __pyx_k_subtract[] = "subtract";
static const char __pyx_k_vonmises[] = "vonmises";
static const char __pyx_k_warnings[] = "warnings";
static const char __pyx_k_RNG_state[] = " RNG state";
static const char __pyx_k_TypeError[] = "TypeError";
static const char __pyx_k_chisquare[] = "chisquare";
static const char __pyx_k_dirichlet[] = "dirichlet";
static const char __pyx_k_enumerate[] = "enumerate";
static const char __pyx_k_geometric[] = "geometric";
static const char __pyx_k_get_state[] = "get_state";
static const char __pyx_k_has_gauss[] = "has_gauss";
static const char __pyx_k_left_mode[] = "left > mode";
static const char __pyx_k_lognormal[] = "lognormal";
static const char __pyx_k_logseries[] = "logseries";
static const char __pyx_k_nsample_1[] = "nsample < 1";
static const char __pyx_k_set_state[] = "set_state";
static const char __pyx_k_threading[] = "threading";
static const char __pyx_k_IndexError[] = "IndexError";
static const char __pyx_k_ValueError[] = "ValueError";
static const char __pyx_k_asanyarray[] = "asanyarray";
static const char __pyx_k_empty_like[] = "empty_like";
static const char __pyx_k_has_uint32[] = "has_uint32";
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_vtable[] = "__pyx_vtable__";
static const char __pyx_k_random_raw[] = "random_raw";
static const char __pyx_k_standard_t[] = "standard_t";
static const char __pyx_k_triangular[] = "triangular";
static const char __pyx_k_MemoryError[] = "MemoryError";
static const char __pyx_k_exponential[] = "exponential";
static const char __pyx_k_gauss_float[] = "gauss_float";
static const char __pyx_k_multinomial[] = "multinomial";
static const char __pyx_k_permutation[] = "permutation";
static const char __pyx_k_randomstate[] = "randomstate";
static const char __pyx_k_RuntimeError[] = "RuntimeError";
static const char __pyx_k_generic_ctor[] = "__generic_ctor";
static const char __pyx_k_noncentral_f[] = "noncentral_f";
static const char __pyx_k_randint_type[] = "_randint_type";
static const char __pyx_k_return_index[] = "return_index";
static const char __pyx_k_searchsorted[] = "searchsorted";
static const char __pyx_k_OverflowError[] = "OverflowError";
static const char __pyx_k_contains_NaNs[] = " contains NaNs";
static const char __pyx_k_count_nonzero[] = "count_nonzero";
static const char __pyx_k_mlfg_1279_861[] = "mlfg-1279-861";
static const char __pyx_k_normal_method[] = "__normal_method";
static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
static const char __pyx_k_random_sample[] = "random_sample";
static const char __pyx_k_RuntimeWarning[] = "RuntimeWarning";
static const char __pyx_k_hypergeometric[] = "hypergeometric";
static const char __pyx_k_random_entropy[] = "random_entropy";
static const char __pyx_k_standard_gamma[] = "standard_gamma";
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_dummy_threading[] = "dummy_threading";
static const char __pyx_k_mlfg_1279_861_2[] = "mlfg_1279_861";
static const char __pyx_k_poisson_lam_max[] = "poisson_lam_max";
static const char __pyx_k_random_integers[] = "random_integers";
static const char __pyx_k_standard_cauchy[] = "standard_cauchy";
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_value_too_large[] = " value too large";
static const char __pyx_k_negative_binomial[] = "negative_binomial";
static const char __pyx_k_DeprecationWarning[] = "DeprecationWarning";
static const char __pyx_k_ngood_nbad_nsample[] = "ngood + nbad < nsample";
static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
static const char __pyx_k_NotImplementedError[] = "NotImplementedError";
static const char __pyx_k_a_must_be_non_empty[] = "a must be non-empty";
static const char __pyx_k_multivariate_normal[] = "multivariate_normal";
static const char __pyx_k_randomstate_entropy[] = "randomstate.entropy";
static const char __pyx_k_noncentral_chisquare[] = "noncentral_chisquare";
static const char __pyx_k_standard_exponential[] = "standard_exponential";
static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
static const char __pyx_k_reset_state_variables[] = "_reset_state_variables";
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_RandomState_f_line_1874[] = "RandomState.f (line 1874)";
static const char __pyx_k_a_must_be_1_dimensional[] = "a must be 1-dimensional";
static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
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_No_vector_path_available[] = "No vector path available";
static const char __pyx_k_a_must_be_greater_than_0[] = "a must be greater than 0";
static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
static const char __pyx_k_RandomState_bytes_line_881[] = "RandomState.bytes (line 881)";
static const char __pyx_k_RandomState_rand_line_1197[] = "RandomState.rand (line 1197)";
static const char __pyx_k_RandomState_wald_line_3180[] = "RandomState.wald (line 3180)";
static const char __pyx_k_RandomState_zipf_line_3585[] = "RandomState.zipf (line 3585)";
static const char __pyx_k_Range_exceeds_valid_bounds[] = "Range exceeds valid bounds";
static const char __pyx_k_low_is_out_of_bounds_for_s[] = "low is out of bounds for %s";
static const char __pyx_k_mean_must_be_1_dimensional[] = "mean must be 1 dimensional";
static const char __pyx_k_RandomState_choice_line_908[] = "RandomState.choice (line 908)";
static const char __pyx_k_RandomState_gamma_line_1796[] = "RandomState.gamma (line 1796)";
static const char __pyx_k_RandomState_power_line_2623[] = "RandomState.power (line 2623)";
static const char __pyx_k_RandomState_randn_line_1245[] = "RandomState.randn (line 1245)";
static const char __pyx_k_a_and_p_must_have_same_size[] = "a and p must have same size";
static const char __pyx_k_high_is_out_of_bounds_for_s[] = "high is out of bounds for %s";
static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
static const char __pyx_k_RandomState_gumbel_line_2807[] = "RandomState.gumbel (line 2807)";
static const char __pyx_k_RandomState_normal_line_1468[] = "RandomState.normal (line 1468)";
static const char __pyx_k_RandomState_pareto_line_2427[] = "RandomState.pareto (line 2427)";
static const char __pyx_k_RandomState_randint_line_783[] = "RandomState.randint (line 783)";
static const char __pyx_k_RandomState_laplace_line_2723[] = "RandomState.laplace (line 2723)";
static const char __pyx_k_RandomState_poisson_line_3514[] = "RandomState.poisson (line 3514)";
static const char __pyx_k_RandomState_shuffle_line_4309[] = "RandomState.shuffle (line 4309)";
static const char __pyx_k_RandomState_tomaxint_line_719[] = "RandomState.tomaxint (line 719)";
static const char __pyx_k_RandomState_uniform_line_1090[] = "RandomState.uniform (line 1090)";
static const char __pyx_k_RandomState_weibull_line_2525[] = "RandomState.weibull (line 2525)";
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_RandomState_binomial_line_3348[] = "RandomState.binomial (line 3348)";
static const char __pyx_k_RandomState_logistic_line_2924[] = "RandomState.logistic (line 2924)";
static const char __pyx_k_RandomState_rayleigh_line_3113[] = "RandomState.rayleigh (line 3113)";
static const char __pyx_k_RandomState_vonmises_line_2344[] = "RandomState.vonmises (line 2344)";
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. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\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,\n            The drawn samples, of shape (size, alpha.ndim).\n\n        Notes\n        -----\n        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n        Uses the following property for computation: for each dimension,\n        draw a random sample y_i from a standard gamma generator of shape\n        `alpha_i`, then\n        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n        Dirichlet distributed.\n\n        References\n        ----------\n        .. [1] David McKay, \"Information Theory, Inference and Learning\n               Algorithms,\" chapter 23,\n               http://www.inference.phy.cam.ac.uk/mackay/\n        .. [2] Wikipedia, \"Dirichlet distribution\",\n               http://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        th""e pieces.\n\n        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\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.\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.). \"Han""dbook 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               http://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.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, normed=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_permutation_x_Randomly_permute[] = "\n        permutation(x)\n\n        Randomly permute a sequence, or return a permuted range.\n\n        If `x` is a multi-dimensional array, it is only shuffled along its\n        first index.\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\n        Returns\n        -------\n        out : ndarray\n            Permuted sequence or array range.\n\n        Examples\n        --------\n        >>> np.random.permutation(10)\n        array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n        >>> np.random.permutation([1, 4, 9, 12, 15])\n        array([15,  1,  9,  4, 12])\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.permutation(arr)\n        array([[6, 7, 8],\n               [0, 1, 2],\n               [3, 4, 5]])\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, should 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 long 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               http://en.wikipedia.org/wiki/Poisson_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> import numpy as np\n        >>> s = np.random.poisson(5, 10000)\n\n        Display histo""gram of the sample:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n        >>> plt.show()\n\n        Draw each 100 values for lambda 100 and 500:\n\n        >>> s = np.random.poisson(lam=(100., 500.), size=(100, 2))\n\n        ";
static const char __pyx_k_randn_d0_d1_dn_method_bm_dtype[] = "\n        randn(d0, d1, ..., dn, method='bm', dtype='float64')\n\n        Return a sample (or samples) from the \"standard normal\" distribution.\n\n        If positive, int_like or int-convertible arguments are provided,\n        `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n        with random floats sampled from a univariate \"normal\" (Gaussian)\n        distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n        floats, they are first converted to integers by truncation). A single\n        float randomly sampled from the distribution is returned if no\n        argument is provided.\n\n        This is a convenience function.  If you want an interface that takes a\n        tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should be all positive.\n            If no argument is given a single Python float is returned.\n        method : str, optional\n            Either 'bm' or 'zig'. 'bm' uses the default Box-Muller transformations\n            method.  'zig' uses the much faster Ziggurat method of Marsaglia and Tsang.\n        dtype : dtype, optional\n            Desired dtype of the result. All dtypes are determined by their\n            name, either 'float64' or 'float32'. The default value is\n            'float64'.\n\n        Returns\n        -------\n        Z : ndarray or float\n            A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n            the standard normal distribution, or a single such float if\n            no parameters were supplied.\n\n        See Also\n        --------\n        random.standard_normal : Similar, but takes a tuple as its argument.\n\n        Notes\n        -----\n        For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n        ``sigma * np.random.randn(...) + mu``\n\n        Examples\n        --------""\n        >>> np.random.randn()\n        2.1923875335537315 #random\n\n        Two-by-four array of samples from N(3, 6.25):\n\n        >>> 2.5 * np.random.randn(2, 4) + 3\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_shuffle_x_Modify_a_sequence_in[] = "\n        shuffle(x)\n\n        Modify a sequence in-place by shuffling its contents.\n\n        This function only shuffles the array along the first axis of a\n        multi-dimensional array. The order of sub-arrays is changed but\n        their contents remains the same.\n\n        Parameters\n        ----------\n        x : array_like\n            The array or list to be shuffled.\n\n        Returns\n        -------\n        None\n\n        Examples\n        --------\n        >>> arr = np.arange(10)\n        >>> np.random.shuffle(arr)\n        >>> arr\n        [1 7 5 2 9 4 3 6 0 8]\n\n        Multi-dimensional arrays are only shuffled along the first axis:\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.shuffle(arr)\n        >>> arr\n        array([[3, 4, 5],\n               [6, 7, 8],\n               [0, 1, 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              http://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              http://en.wikipedia.org/wiki/C""auchy_distribution\n\n        Examples\n        --------\n        Draw samples and plot the distribution:\n\n        >>> s = np.random.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)\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. All dtypes are determined by their\n            name, either 'float64' or 'float32'. The default value is\n            'float64'.\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.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)\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, should 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 ``shape`` is a scalar.  Otherwise,\n            ``np.array(shape).size`` samples are drawn.\n        dtype : dtype, optional\n            Desired dtype of the result, either ``np.float64`` (default)\n            or ``np.float32``.\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.distributions.gamma : probability density function,\n            distribution or 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      ""         http://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.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\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\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, should be > 0.\n        scale : float or array_like of floats\n            Scale parameter, should 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://www.brighton-webs.co.uk/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, \"Wald distribution\"\n               http://en.wikipedia.org/wiki/Wald_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.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_RandomState_chisquare_line_2034[] = "RandomState.chisquare (line 2034)";
static const char __pyx_k_RandomState_dirichlet_line_4188[] = "RandomState.dirichlet (line 4188)";
static const char __pyx_k_RandomState_geometric_line_3664[] = "RandomState.geometric (line 3664)";
static const char __pyx_k_RandomState_hypergeometric_line[] = "RandomState.hypergeometric (line 3715)";
static const char __pyx_k_RandomState_lognormal_line_3003[] = "RandomState.lognormal (line 3003)";
static const char __pyx_k_RandomState_logseries_line_3837[] = "RandomState.logseries (line 3837)";
static const char __pyx_k_RandomState_multivariate_normal[] = "RandomState.multivariate_normal (line 3917)";
static const char __pyx_k_RandomState_standard_gamma_line[] = "RandomState.standard_gamma (line 1711)";
static const char __pyx_k_Unsupported_dtype_s_for_randint[] = "Unsupported dtype \"%s\" for randint";
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.distributions.binom : probability density function,\n            distribution or 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 binomial 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               http://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> n, p = 10, .5  # number of trials, probability of each trial\n        >>> s = np.random.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(np.random.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.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 : int or array_like of ints\n             Number of degrees of freedom.\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               http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n\n        Examples\n        --------\n        >>> np.random.chisquare(""2,4)\n        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])\n\n        ";
static const char __pyx_k_choice_a_size_None_replace_True[] = "\n        choice(a, size=None, replace=True, p=None)\n\n        Generates a random sample from a given 1-D array\n\n                .. versionadded:: 1.7.0\n\n        Parameters\n        ----------\n        a : 1-D array-like or int\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated as if a was np.arange(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        replace : boolean, 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\n        Returns\n        -------\n        samples : 1-D ndarray, shape (size,)\n            The generated random samples\n\n        Raises\n        ------\n        ValueError\n            If a is an int and less than zero, if a or p are not 1-dimensional,\n            if a is an array-like of 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        randint, shuffle, permutation\n\n        Examples\n        --------\n        Generate a uniform random sample from np.arange(5) of size 3:\n\n        >>> np.random.choice(5, 3)\n        array([0, 3, 4])\n        >>> #This is equivalent to np.random.randint(0,5,3)\n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n\n        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0])\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n        re""placement:\n\n        >>> np.random.choice(5, 3, replace=False)\n        array([3,1,0])\n        >>> #This is equivalent to np.random.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        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0])\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        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n              dtype='|S11')\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 should 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 : int or array_like of ints\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : int or array_like of ints\n            Degrees of freedom in denominator. Should be greater than zero.\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.distributions.f : probability density function,\n            distribution or 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 sa""mples in each group\n        minus the number 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               http://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.f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> sort(s)[-10]\n        7.61988120985\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. Should be greater than zero.\n        scale : float or array_like of floats, optional\n            The scale of the gamma distribution. Should be greater than zero.\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.distributions.gamma : probability density function,\n            distribution or 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               Wolfr""am Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma distribution\",\n               http://en.wikipedia.org/wiki/Gamma_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 2. # mean and dispersion\n        >>> s = np.random.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\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\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.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.\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 Gumbel (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        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.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, normed=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 compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n    ""    >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=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.\n        nbad : int or array_like of ints\n            Number of ways to make a bad selection.  Must be nonnegative.\n        nsample : int or array_like of ints\n            Number of items sampled.  Must be at least 1 and at most\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.\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them""\n        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        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               http://en.wikipedia.org/wiki/Hypergeometric_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\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 = np.random.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_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. Should be greater than zero.\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.distributions.logistic : probability density function,\n            distribution or 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 Ins""urance, Finance, Hydrology 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               http://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.logistic(loc, scale, 10000)\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n        ... logist(bins, loc, scale).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. Should\n            be greater than zero. 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 *produc""t*\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               http://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        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.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, normed=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        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n        >>> count, bins, ignored = plt.h""ist(b, 100, normed=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.distributions.logser : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density 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               individual""s in a random 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               http://en.wikipedia.org/wiki/Logarithmic_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = .6\n        >>> s = np.random.logseries(a, 10000)\n        >>> count, bins, ignored = plt.hist(s)\n\n        #   plot against distribution\n\n        >>> def logseries(k, p):\n        ...     return -p**k/(k*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 generalisation 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\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            should 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        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])\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        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  Fo""r the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        A loaded die is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5 + [2/7.])\n        array([11, 16, 14, 17, 16, 26])\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        >>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])  # RIGHT\n        array([38, 62])\n\n        not like:\n\n        >>> np.random.multinomial(100, [1.0, 2.0])  # WRONG\n        array([100,   0])\n\n        ";
static const char __pyx_k_multivariate_normal_mean_cov_si[] = "\n        multivariate_normal(mean, cov, size=None, method='bm')\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        method : str, optional\n            Either 'bm' or 'zig'. 'bm' uses the default Box-Muller transformations\n            method.  'zig' uses the much faster Ziggurat method of Marsaglia and Tsang.\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 cur""ve 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.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        >>> x = np.random.multivariate_normal(mean, cov, (3, 3))\n        >>> x.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]\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` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\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 negative binomial distribution,\n            where each sample is equal to N, the number of trials it took to\n            achieve n - 1 successes, N - (n - 1) failures, and a success on the,\n            (N + n)th trial.\n\n        Notes\n        -----\n        The probability density for the negative binomial distribution is\n\n        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n        where :math:`n-1` is the number of successes, :math:`p` is the\n        probability of success, and :math:`N+n-1` is the number of trials.\n        The negative binomial distribution gives the probability of n-1\n        successes and N failures in N+n-1 trials, and success on the (N+n)th\n        trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears,\n        then the 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               http://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.negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11):\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 generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int or array_like of ints\n            Degrees of freedom, should be > 0 as of Numpy 1.10,\n            should be > 1 for earlier versions.\n        nonc : float or array_like of floats\n            Non-centrality, should 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        In Delhi (2007), it is noted that the noncentral chi-square is\n        useful in bombing and coverage problems, the probability of\n        killing the point target given by the noncentral chi-squared\n        distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in\n               the analysis of weapon systems effectiveness\", Metrika,\n               Volume 15, Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               ht""tp://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=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(np.random.noncentral_chisquare(3, .0000001, 100000),\n        ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=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(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=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 : int or array_like of ints\n            Parameter, should be > 1.\n        dfden : int or array_like of ints\n            Parameter, should be > 1.\n        nonc : float or array_like of floats\n            Parameter, should 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               http://en.wikipedia.org/wiki/Noncentral_F-dis""tribution\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        >>> dfnum = 3 # between group deg of freedom\n        >>> dfden = 20 # within groups degrees of freedom\n        >>> nonc = 3.0\n        >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n        >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n        >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n        >>> F = np.histogram(c_vals, bins=50, normed=True)\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, method='bm')\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.\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        method : str, optional\n            Either 'bm' or 'zig'. 'bm' uses the default Box-Muller transformations\n            method.  'zig' uses the much faster Ziggurat method of Marsaglia and Tsang.\n\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.distributions.norm : probability density function,\n            distribution or 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        `numpy.random.normal` is more likely to return samples lying close to\n        the mean, rather than those far away.\n\n        References\n        ----------\n        .. [1] Wikipedia, \"Normal distribution\",\n               http://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.normal(mu, sigma, 1000)\n\n        Verify the mean and the variance:\n\n        >>> abs(mu - np.mean(s)) < 0.01\n        True\n\n        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n        True\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, normed=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        ";
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. Should be greater than zero.\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.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n         ""   distribution or 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               http://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.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, normed=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_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. Should be greater than zero.\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               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> samples = 100""0\n        >>> s = np.random.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\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
static const char __pyx_k_rand_d0_d1_dn_dtype_float64_Ran[] = "\n        rand(d0, d1, ..., dn, dtype='float64')\n\n        Random values in a given shape.\n\n        Create an array of the given shape and populate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should all be positive.\n            If no argument is given a single Python float is returned.\n        dtype : dtype, optional\n            Desired dtype of the result. All dtypes are determined by their\n            name, either 'float64' or 'float32'. The default value is\n            'float64'.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that takes\n        a shape-tuple as the first argument, refer to np.random.random_sample.\n\n        ``dtype`` can only be changed using a keyword argument.\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n        ";
static const char __pyx_k_randint_low_high_None_size_None[] = "\n        randint(low, high=None, size=None, dtype='l')\n\n        Return random integers from `low` (inclusive) to `high` (exclusive).\n\n        Return random integers from the \"discrete uniform\" distribution of\n        the specified dtype in the \"half-open\" interval [`low`, `high`). If\n        `high` is None (the default), then results are from [0, `low`).\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, 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        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. All dtypes are determined by their\n            name, i.e., 'int64', 'int', etc, so byteorder is not available\n            and a specific precision may have different C types depending\n            on the platform. The default value is 'np.int'.\n\n            .. versionadded:: 1.11.0\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        See Also\n        --------\n        random.random_integers : similar to `randint`, only for the closed\n            interval [`low`, `high`], and 1 is the lowest value if `high` is\n            omitted. In particular, this other one is the one to use to generate\n            uniformly distributed discrete non-integers.\n\n        Examples\n        --------\n   ""     >>> np.random.randint(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n        >>> np.random.randint(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        >>> np.random.randint(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])\n\n        ";
static const char __pyx_k_random_integers_low_high_None_s[] = "\n        random_integers(low, high=None, size=None)\n\n        Random integers of type np.int between `low` and `high`, inclusive.\n\n        Return random integers of type np.int from the \"discrete uniform\"\n        distribution in the closed interval [`low`, `high`].  If `high` is\n        None (the default), then results are from [1, `low`]. The np.int\n        type translates to the C long type used by Python 2 for \"short\"\n        integers and its precision is platform dependent.\n\n        This function has been deprecated. Use randint instead.\n\n        .. deprecated:: 1.11.0\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, the largest (signed) integer to be drawn from the\n            distribution (see above for behavior if ``high=None``).\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 : 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        See Also\n        --------\n        random.randint : Similar to `random_integers`, only for the half-open\n            interval [`low`, `high`), and 0 is the lowest value if `high` is\n            omitted.\n\n        Notes\n        -----\n        To sample from N evenly spaced floating-point numbers between a and b,\n        use::\n\n          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n        Examples\n        --------\n        >>> np.random.random_integers(5)\n        4\n        >"">> type(np.random.random_integers(5))\n        <type 'int'>\n        >>> np.random.random_integers(5, size=(3.,2.))\n        array([[5, 4],\n               [3, 3],\n               [4, 5]])\n\n        Choose five random numbers from the set of five evenly-spaced\n        numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n\n        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])\n\n        Roll two six sided dice 1000 times and sum the results:\n\n        >>> d1 = np.random.random_integers(1, 6, 1000)\n        >>> d2 = np.random.random_integers(1, 6, 1000)\n        >>> dsums = d1 + d2\n\n        Display results as a histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_random_sample_size_None_dtype_d[] = "\n        random_sample(size=None, dtype='d')\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_sample` by `(b-a)` and add `a`::\n\n          (b - a) * random_sample() + 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. All dtypes are determined by their\n            name, either 'float64' or 'float32'. The default value is\n            'float64'.\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        >>> np.random.random_sample()\n        0.47108547995356098\n        >>> type(np.random.random_sample())\n        <type 'float'>\n        >>> np.random.random_sample((5,))\n        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])\n\n        Three-by-two array of random numbers from [-5, 0):\n\n        >>> 5 * np.random.random_sample((3, 2)) - 5\n        array([[-3.99149989, -0.52338984],\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. Should be >= 0. 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               http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        .. [2] Wikipedia, \"Rayleigh distribution\"\n               http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=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        >>> mo""devalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
static const char __pyx_k_standard_normal_size_None_dtype[] = "\n        standard_normal(size=None, dtype='d', method='bm')\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. All dtypes are determined by their\n            name, either 'float64' or 'float32'. The default value is\n            'float64'.\n        method : str, optional\n            Either 'bm' or 'zig'. 'bm' uses the default Box-Muller transformations\n            method.  'zig' uses the much faster Ziggurat method of Marsaglia and Tsang.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        >>> s = np.random.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 = np.random.standard_normal(size=(3, 4, 2))\n        >>> s.shape\n        (3, 4, 2)\n\n        Notes\n        -----\n        float32 normals are only available using the Box-Muller transformation\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 : int or array_like of ints\n            Degrees of freedom, should 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 Gisset 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               http://en.wikipedia.org/wiki/Student's_t-distributio""n\n\n        Examples\n        --------\n        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n        women in 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.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, normed=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_tomaxint_size_None_Random_integ[] = "\n        tomaxint(size=None)\n\n        Random integers between 0 and ``sys.maxint``, inclusive.\n\n        Return a sample of uniformly distributed random integers in the interval\n        [0, ``sys.maxint``].\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        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Uniform sampling over a given half-open interval of integers.\n        random_integers : Uniform sampling over a given closed interval of\n            integers.\n\n        Examples\n        --------\n        >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n        >>> RS.tomaxint((2,2,2))\n        array([[[1170048599, 1600360186],\n                [ 739731006, 1947757578]],\n               [[1871712945,  752307660],\n                [1601631370, 1479324245]]])\n        >>> import sys\n        >>> sys.maxint\n        2147483647\n        >>> RS.tomaxint((2,2,2)) < sys.maxint\n        array([[[ True,  True],\n                [ True,  True]],\n               [[ True,  True],\n                [ True,  True]]], dtype=bool)\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 should fulfill the condition ``left <= mode <= right``.\n        right : float or array_like of floats\n            Upper limit, should 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       "" References\n        ----------\n        .. [1] Wikipedia, \"Triangular distribution\"\n               http://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.triangular(-3, 0, 8, 100000), bins=200,\n        ...              normed=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        randint : Discrete uniform distribution, yielding integers.\n        random_integers : Discrete uniform distribution over the closed\n                          interval ``[low, high]``.\n        random_sample : Floats uniformly distributed over ``[0, 1)``.\n        random : Alias for `random_sample`.\n        rand : Convenience function that accepts dimensions as input, e.g.,\n               ``rand(2,2)`` would generate a 2-by-2 array of floats,\n               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        anywhe""re 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        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> s = np.random.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, normed=True)\n        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n        >>> plt.show()\n        ";
static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
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.distributions.vonmises : probability density function,\n            distribution, or 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        probabilit""y theory, aerodynamics, 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.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\n        >>> plt.hist(s, 50, normed=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))\n        >>> plt.plot(x, y, linewidth=2, color='r')\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 of the distribution. Should be greater than zero.\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.distributions.weibull_max\n        scipy.stats.distributions.weibull_min\n        scipy.stats.distributions.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               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.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(np.random.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. Should 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.distributions.zipf : probability density function,\n            distribution, or 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.r""andom.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\n\n        Truncate s values at 50 so plot is interesting:\n\n        >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n        >>> x = np.arange(1., 50.)\n        >>> y = x**(-a) / special.zetac(a)\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\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_take_a_larger_sample_than[] = "Cannot take a larger sample than population when 'replace=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_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd";
static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported";
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_RandomState_multinomial_line_406[] = "RandomState.multinomial (line 4068)";
static const char __pyx_k_RandomState_negative_binomial_li[] = "RandomState.negative_binomial (line 3438)";
static const char __pyx_k_RandomState_noncentral_chisquare[] = "RandomState.noncentral_chisquare (line 2102)";
static const char __pyx_k_RandomState_noncentral_f_line_19[] = "RandomState.noncentral_f (line 1962)";
static const char __pyx_k_RandomState_permutation_line_439[] = "RandomState.permutation (line 4396)";
static const char __pyx_k_RandomState_random_integers_line[] = "RandomState.random_integers (line 1309)";
static const char __pyx_k_RandomState_random_sample_line_6[] = "RandomState.random_sample (line 665)";
static const char __pyx_k_RandomState_standard_cauchy_line[] = "RandomState.standard_cauchy (line 2187)";
static const char __pyx_k_RandomState_standard_exponential[] = "RandomState.standard_exponential (line 1666)";
static const char __pyx_k_RandomState_standard_normal_line[] = "RandomState.standard_normal (line 1406)";
static const char __pyx_k_RandomState_standard_t_line_2251[] = "RandomState.standard_t (line 2251)";
static const char __pyx_k_RandomState_triangular_line_3248[] = "RandomState.triangular (line 3248)";
static const char __pyx_k_Seed_values_must_be_between_0_an[] = "Seed values must be between 0 and 18446744073709551616 (2**64-1)";
static const char __pyx_k_Support_for_multiple_version_has[] = "Support for multiple version has not been implemented.";
static const char __pyx_k_This_function_is_deprecated_Plea[] = "This function is deprecated. Please call randint(1, {low} + 1) instead";
static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
static const char __pyx_k_Unsupported_dtype_s_for_random_s[] = "Unsupported dtype \"%s\" for random_sample";
static const char __pyx_k_Unsupported_dtype_s_for_standard[] = "Unsupported dtype \"%s\" for standard_normal";
static const char __pyx_k_a_must_be_1_dimensional_or_an_in[] = "a must be 1-dimensional or an integer";
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_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous";
static const char __pyx_k_probabilities_are_not_non_negati[] = "probabilities are not non-negative";
static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short.";
static const char __pyx_k_This_function_is_deprecated_Plea_2[] = "This function is deprecated. Please call randint({low}, {high} + 1) instead";
static const char __pyx_k_Unsupported_dtype_s_for_standard_2[] = "Unsupported dtype \"%s\" for standard_exponential";
static const char __pyx_k_Unsupported_dtype_s_for_standard_3[] = "Unsupported dtype \"%s\" for standard_gamma";
static PyObject *__pyx_kp_s_0;
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static PyObject *__pyx_kp_s_0_or;
static PyObject *__pyx_kp_s_1;
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static PyObject *__pyx_kp_s_Cannot_take_a_larger_sample_than;
static PyObject *__pyx_n_s_DeprecationWarning;
static PyObject *__pyx_n_s_Ellipsis;
static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
static PyObject *__pyx_kp_s_Fewer_non_zero_entries_in_p_than;
static PyObject *__pyx_kp_u_Format_string_allocated_too_shor;
static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;
static PyObject *__pyx_n_s_IndexError;
static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
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static PyObject *__pyx_n_s_MAXSIZE;
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_kp_s_No_vector_path_available;
static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;
static PyObject *__pyx_n_s_NotImplementedError;
static PyObject *__pyx_kp_s_Not_a;
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_kp_s_RNG_state;
static PyObject *__pyx_kp_u_RandomState_binomial_line_3348;
static PyObject *__pyx_kp_u_RandomState_bytes_line_881;
static PyObject *__pyx_kp_u_RandomState_chisquare_line_2034;
static PyObject *__pyx_kp_u_RandomState_choice_line_908;
static PyObject *__pyx_kp_u_RandomState_dirichlet_line_4188;
static PyObject *__pyx_kp_u_RandomState_f_line_1874;
static PyObject *__pyx_kp_u_RandomState_gamma_line_1796;
static PyObject *__pyx_kp_u_RandomState_geometric_line_3664;
static PyObject *__pyx_kp_u_RandomState_gumbel_line_2807;
static PyObject *__pyx_kp_u_RandomState_hypergeometric_line;
static PyObject *__pyx_kp_u_RandomState_laplace_line_2723;
static PyObject *__pyx_kp_u_RandomState_logistic_line_2924;
static PyObject *__pyx_kp_u_RandomState_lognormal_line_3003;
static PyObject *__pyx_kp_u_RandomState_logseries_line_3837;
static PyObject *__pyx_kp_u_RandomState_multinomial_line_406;
static PyObject *__pyx_kp_u_RandomState_multivariate_normal;
static PyObject *__pyx_kp_u_RandomState_negative_binomial_li;
static PyObject *__pyx_kp_u_RandomState_noncentral_chisquare;
static PyObject *__pyx_kp_u_RandomState_noncentral_f_line_19;
static PyObject *__pyx_kp_u_RandomState_normal_line_1468;
static PyObject *__pyx_kp_u_RandomState_pareto_line_2427;
static PyObject *__pyx_kp_u_RandomState_permutation_line_439;
static PyObject *__pyx_kp_u_RandomState_poisson_line_3514;
static PyObject *__pyx_kp_u_RandomState_power_line_2623;
static PyObject *__pyx_kp_u_RandomState_rand_line_1197;
static PyObject *__pyx_kp_u_RandomState_randint_line_783;
static PyObject *__pyx_kp_u_RandomState_randn_line_1245;
static PyObject *__pyx_kp_u_RandomState_random_integers_line;
static PyObject *__pyx_kp_u_RandomState_random_sample_line_6;
static PyObject *__pyx_kp_u_RandomState_rayleigh_line_3113;
static PyObject *__pyx_kp_u_RandomState_shuffle_line_4309;
static PyObject *__pyx_kp_u_RandomState_standard_cauchy_line;
static PyObject *__pyx_kp_u_RandomState_standard_exponential;
static PyObject *__pyx_kp_u_RandomState_standard_gamma_line;
static PyObject *__pyx_kp_u_RandomState_standard_normal_line;
static PyObject *__pyx_kp_u_RandomState_standard_t_line_2251;
static PyObject *__pyx_kp_u_RandomState_tomaxint_line_719;
static PyObject *__pyx_kp_u_RandomState_triangular_line_3248;
static PyObject *__pyx_kp_u_RandomState_uniform_line_1090;
static PyObject *__pyx_kp_u_RandomState_vonmises_line_2344;
static PyObject *__pyx_kp_u_RandomState_wald_line_3180;
static PyObject *__pyx_kp_u_RandomState_weibull_line_2525;
static PyObject *__pyx_kp_u_RandomState_zipf_line_3585;
static PyObject *__pyx_kp_s_Range_exceeds_valid_bounds;
static PyObject *__pyx_n_s_RuntimeError;
static PyObject *__pyx_n_s_RuntimeWarning;
static PyObject *__pyx_kp_s_Seed_values_must_be_between_0_an;
static PyObject *__pyx_kp_s_Support_for_multiple_version_has;
static PyObject *__pyx_n_s_T;
static PyObject *__pyx_kp_s_This_function_is_deprecated_Plea;
static PyObject *__pyx_kp_s_This_function_is_deprecated_Plea_2;
static PyObject *__pyx_n_s_TypeError;
static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
static PyObject *__pyx_kp_s_Unsupported_dtype_s_for_randint;
static PyObject *__pyx_kp_s_Unsupported_dtype_s_for_random_s;
static PyObject *__pyx_kp_s_Unsupported_dtype_s_for_standard;
static PyObject *__pyx_kp_s_Unsupported_dtype_s_for_standard_2;
static PyObject *__pyx_kp_s_Unsupported_dtype_s_for_standard_3;
static PyObject *__pyx_n_s_ValueError;
static PyObject *__pyx_kp_s__46;
static PyObject *__pyx_n_s_a;
static PyObject *__pyx_kp_s_a_and_p_must_have_same_size;
static PyObject *__pyx_kp_s_a_must_be_1_dimensional;
static PyObject *__pyx_kp_s_a_must_be_1_dimensional_or_an_in;
static PyObject *__pyx_kp_s_a_must_be_greater_than_0;
static PyObject *__pyx_kp_s_a_must_be_non_empty;
static PyObject *__pyx_n_s_add;
static PyObject *__pyx_n_s_all;
static PyObject *__pyx_n_s_allocate_buffer;
static PyObject *__pyx_n_s_alpha;
static PyObject *__pyx_n_s_any;
static PyObject *__pyx_n_s_arange;
static PyObject *__pyx_n_s_array;
static PyObject *__pyx_n_s_asanyarray;
static PyObject *__pyx_n_s_asarray;
static PyObject *__pyx_n_s_astype;
static PyObject *__pyx_n_s_axis;
static PyObject *__pyx_n_s_b;
static PyObject *__pyx_n_s_base;
static PyObject *__pyx_n_s_beta;
static PyObject *__pyx_n_s_binomial;
static PyObject *__pyx_kp_u_binomial_n_p_size_None_Draw_sam;
static PyObject *__pyx_n_s_bm;
static PyObject *__pyx_n_s_bool;
static PyObject *__pyx_n_s_bool_2;
static PyObject *__pyx_n_s_bytes;
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_casting;
static PyObject *__pyx_n_s_chisquare;
static PyObject *__pyx_kp_u_chisquare_df_size_None_Draw_sam;
static PyObject *__pyx_n_s_choice;
static PyObject *__pyx_kp_u_choice_a_size_None_replace_True;
static PyObject *__pyx_n_s_chr;
static PyObject *__pyx_n_s_class;
static PyObject *__pyx_kp_s_contains_NaNs;
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_s_count_nonzero;
static PyObject *__pyx_n_s_cov;
static PyObject *__pyx_kp_s_cov_must_be_2_dimensional_and_sq;
static PyObject *__pyx_kp_s_covariance_is_not_positive_semid;
static PyObject *__pyx_n_s_ctypes;
static PyObject *__pyx_n_s_cumsum;
static PyObject *__pyx_n_s_data;
static PyObject *__pyx_n_s_df;
static PyObject *__pyx_n_s_dfden;
static PyObject *__pyx_n_s_dfnum;
static PyObject *__pyx_n_s_dirichlet;
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_dummy_threading;
static PyObject *__pyx_n_s_empty;
static PyObject *__pyx_n_s_empty_like;
static PyObject *__pyx_n_s_encode;
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_n_s_exponential;
static PyObject *__pyx_n_s_f;
static PyObject *__pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa;
static PyObject *__pyx_n_s_fallback;
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_gamma;
static PyObject *__pyx_kp_u_gamma_shape_scale_1_0_size_None;
static PyObject *__pyx_n_s_gauss;
static PyObject *__pyx_n_s_gauss_float;
static PyObject *__pyx_n_s_generic_ctor;
static PyObject *__pyx_n_s_geometric;
static PyObject *__pyx_kp_u_geometric_p_size_None_Draw_samp;
static PyObject *__pyx_n_s_get_state;
static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
static PyObject *__pyx_n_s_greater;
static PyObject *__pyx_n_s_gumbel;
static PyObject *__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N;
static PyObject *__pyx_n_s_has_gauss;
static PyObject *__pyx_n_s_has_uint32;
static PyObject *__pyx_n_s_high;
static PyObject *__pyx_kp_s_high_is_out_of_bounds_for_s;
static PyObject *__pyx_n_s_hypergeometric;
static PyObject *__pyx_kp_u_hypergeometric_ngood_nbad_nsamp;
static PyObject *__pyx_n_s_id;
static PyObject *__pyx_n_s_iinfo;
static PyObject *__pyx_n_s_import;
static PyObject *__pyx_n_s_index;
static PyObject *__pyx_n_s_int;
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_intp;
static PyObject *__pyx_n_s_isfinite;
static PyObject *__pyx_n_s_isnan;
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_join;
static PyObject *__pyx_n_s_kappa;
static PyObject *__pyx_n_s_l;
static PyObject *__pyx_n_s_lam;
static PyObject *__pyx_n_s_laplace;
static PyObject *__pyx_kp_u_laplace_loc_0_0_scale_1_0_size;
static PyObject *__pyx_n_s_left;
static PyObject *__pyx_kp_s_left_mode;
static PyObject *__pyx_kp_s_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_s_logical_or;
static PyObject *__pyx_n_s_logistic;
static PyObject *__pyx_kp_u_logistic_loc_0_0_scale_1_0_size;
static PyObject *__pyx_n_s_lognormal;
static PyObject *__pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si;
static PyObject *__pyx_n_s_logseries;
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_kp_s_low_high;
static PyObject *__pyx_kp_s_low_is_out_of_bounds_for_s;
static PyObject *__pyx_n_s_main;
static PyObject *__pyx_n_s_map;
static PyObject *__pyx_n_s_max;
static PyObject *__pyx_n_s_maxsize;
static PyObject *__pyx_n_s_mean;
static PyObject *__pyx_kp_s_mean_and_cov_must_have_same_leng;
static PyObject *__pyx_kp_s_mean_must_be_1_dimensional;
static PyObject *__pyx_n_s_memview;
static PyObject *__pyx_n_s_method;
static PyObject *__pyx_kp_u_mlfg_1279_861;
static PyObject *__pyx_n_u_mlfg_1279_861_2;
static PyObject *__pyx_n_s_mode;
static PyObject *__pyx_kp_s_mode_right;
static PyObject *__pyx_n_s_mu;
static PyObject *__pyx_n_s_multinomial;
static PyObject *__pyx_kp_u_multinomial_n_pvals_size_None_D;
static PyObject *__pyx_n_s_multivariate_normal;
static PyObject *__pyx_kp_u_multivariate_normal_mean_cov_si;
static PyObject *__pyx_n_s_n;
static PyObject *__pyx_n_s_name;
static PyObject *__pyx_n_s_name_2;
static PyObject *__pyx_n_s_nbad;
static PyObject *__pyx_kp_s_nbad_0;
static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;
static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;
static PyObject *__pyx_n_s_ndim;
static PyObject *__pyx_n_s_negative_binomial;
static PyObject *__pyx_kp_u_negative_binomial_n_p_size_None;
static PyObject *__pyx_n_s_ngood;
static PyObject *__pyx_kp_s_ngood_0;
static PyObject *__pyx_kp_s_ngood_nbad_nsample;
static PyObject *__pyx_n_s_nonc;
static PyObject *__pyx_n_s_noncentral_chisquare;
static PyObject *__pyx_kp_u_noncentral_chisquare_df_nonc_si;
static PyObject *__pyx_n_s_noncentral_f;
static PyObject *__pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s;
static PyObject *__pyx_n_s_normal;
static PyObject *__pyx_kp_u_normal_loc_0_0_scale_1_0_size_N;
static PyObject *__pyx_n_s_normal_method;
static PyObject *__pyx_n_s_np;
static PyObject *__pyx_n_s_nsample;
static PyObject *__pyx_kp_s_nsample_1;
static PyObject *__pyx_n_s_numpy;
static PyObject *__pyx_n_s_numpy_dual;
static PyObject *__pyx_n_s_obj;
static PyObject *__pyx_n_s_object;
static PyObject *__pyx_n_s_operator;
static PyObject *__pyx_n_s_p;
static PyObject *__pyx_kp_s_p_must_be_1_dimensional;
static PyObject *__pyx_n_s_pack;
static PyObject *__pyx_n_s_pareto;
static PyObject *__pyx_kp_u_pareto_a_size_None_Draw_samples;
static PyObject *__pyx_n_s_permutation;
static PyObject *__pyx_kp_u_permutation_x_Randomly_permute;
static PyObject *__pyx_n_s_poisson;
static PyObject *__pyx_kp_u_poisson_lam_1_0_size_None_Draw;
static PyObject *__pyx_n_s_poisson_lam_max;
static PyObject *__pyx_n_s_power;
static PyObject *__pyx_kp_u_power_a_size_None_Draws_samples;
static PyObject *__pyx_n_s_prng;
static PyObject *__pyx_kp_s_probabilities_are_not_non_negati;
static PyObject *__pyx_kp_s_probabilities_do_not_sum_to_1;
static PyObject *__pyx_n_s_prod;
static PyObject *__pyx_n_s_pvals;
static PyObject *__pyx_n_s_pyx_getbuffer;
static PyObject *__pyx_n_s_pyx_vtable;
static PyObject *__pyx_n_s_rand;
static PyObject *__pyx_n_s_rand_2;
static PyObject *__pyx_kp_u_rand_d0_d1_dn_dtype_float64_Ran;
static PyObject *__pyx_n_s_randint;
static PyObject *__pyx_kp_u_randint_low_high_None_size_None;
static PyObject *__pyx_n_s_randint_type;
static PyObject *__pyx_n_s_randn;
static PyObject *__pyx_kp_u_randn_d0_d1_dn_method_bm_dtype;
static PyObject *__pyx_n_s_random;
static PyObject *__pyx_n_s_random_entropy;
static PyObject *__pyx_n_s_random_integers;
static PyObject *__pyx_kp_u_random_integers_low_high_None_s;
static PyObject *__pyx_n_s_random_raw;
static PyObject *__pyx_n_s_random_sample;
static PyObject *__pyx_kp_u_random_sample_size_None_dtype_d;
static PyObject *__pyx_n_s_randomstate;
static PyObject *__pyx_n_s_randomstate_entropy;
static PyObject *__pyx_n_s_ranf;
static PyObject *__pyx_n_s_range;
static PyObject *__pyx_n_s_ravel;
static PyObject *__pyx_n_s_rayleigh;
static PyObject *__pyx_kp_u_rayleigh_scale_1_0_size_None_Dr;
static PyObject *__pyx_n_s_reduce;
static PyObject *__pyx_n_s_replace;
static PyObject *__pyx_n_s_reset_state_variables;
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_s_safe;
static PyObject *__pyx_n_s_sample;
static PyObject *__pyx_n_s_scale;
static PyObject *__pyx_n_s_searchsorted;
static PyObject *__pyx_n_s_seed;
static PyObject *__pyx_kp_s_seed_0;
static PyObject *__pyx_n_s_set_state;
static PyObject *__pyx_n_s_shape;
static PyObject *__pyx_n_s_shuffle;
static PyObject *__pyx_kp_u_shuffle_x_Modify_a_sequence_in;
static PyObject *__pyx_n_s_side;
static PyObject *__pyx_n_s_sigma;
static PyObject *__pyx_n_s_signbit;
static PyObject *__pyx_n_s_size;
static PyObject *__pyx_n_s_sort;
static PyObject *__pyx_n_s_sqrt;
static PyObject *__pyx_n_s_squeeze;
static PyObject *__pyx_n_s_standard_cauchy;
static PyObject *__pyx_kp_u_standard_cauchy_size_None_Draw;
static PyObject *__pyx_n_s_standard_exponential;
static PyObject *__pyx_kp_u_standard_exponential_size_None;
static PyObject *__pyx_n_s_standard_gamma;
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_n_s_standard_t;
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_stream;
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_n_s_struct;
static PyObject *__pyx_n_s_subtract;
static PyObject *__pyx_n_s_sum;
static PyObject *__pyx_kp_s_sum_pvals_1_1_0;
static PyObject *__pyx_n_s_svd;
static PyObject *__pyx_n_s_sys;
static PyObject *__pyx_n_s_take;
static PyObject *__pyx_n_s_test;
static PyObject *__pyx_n_s_threading;
static PyObject *__pyx_n_s_tobytes;
static PyObject *__pyx_kp_u_tomaxint_size_None_Random_integ;
static PyObject *__pyx_n_s_triangular;
static PyObject *__pyx_kp_u_triangular_left_mode_right_size;
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_n_s_uniform;
static PyObject *__pyx_kp_u_uniform_low_0_0_high_1_0_size_N;
static PyObject *__pyx_n_s_unique;
static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
static PyObject *__pyx_n_s_unpack;
static PyObject *__pyx_n_s_unsafe;
static PyObject *__pyx_kp_s_value_too_large;
static PyObject *__pyx_n_s_version;
static PyObject *__pyx_n_s_vonmises;
static PyObject *__pyx_kp_u_vonmises_mu_kappa_size_None_Dra;
static PyObject *__pyx_n_s_wald;
static PyObject *__pyx_kp_u_wald_mean_scale_size_None_Draw;
static PyObject *__pyx_n_s_warn;
static PyObject *__pyx_n_s_warnings;
static PyObject *__pyx_n_s_weibull;
static PyObject *__pyx_kp_u_weibull_a_size_None_Draw_sample;
static PyObject *__pyx_n_s_zeros;
static PyObject *__pyx_n_u_zig;
static PyObject *__pyx_n_s_zipf;
static PyObject *__pyx_kp_u_zipf_a_size_None_Draw_samples_f;
static int __pyx_pf_11randomstate_13mlfg_1279_861_11RandomState___init__(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
static void __pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_2__dealloc__(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_4seed(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_6_reset_state_variables(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_8get_state(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_10set_state(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_12random_raw(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_14__getstate__(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_16__setstate__(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_18__reduce__(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_20random_sample(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_22tomaxint(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_24randint(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_26bytes(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, npy_intp __pyx_v_length); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_28choice(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size, PyObject *__pyx_v_replace, PyObject *__pyx_v_p); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_30uniform(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_32rand(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_dtype, PyObject *__pyx_v_args); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_34randn(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_method, PyObject *__pyx_v_dtype, PyObject *__pyx_v_args); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_36random_integers(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_38standard_normal(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype, PyObject *__pyx_v_method); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_40normal(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size, PyObject *__pyx_v_method); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_42beta(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_44exponential(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_46standard_exponential(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_48standard_gamma(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_size, PyObject *__pyx_v_dtype); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_50gamma(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_52f(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_54noncentral_f(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_56chisquare(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_58noncentral_chisquare(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_60standard_cauchy(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_62standard_t(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_64vonmises(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_mu, PyObject *__pyx_v_kappa, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_66pareto(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_68weibull(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_70power(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_72laplace(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_74gumbel(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_76logistic(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_78lognormal(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_sigma, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_80rayleigh(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_82wald(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_84triangular(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_left, PyObject *__pyx_v_mode, PyObject *__pyx_v_right, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_86binomial(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_88negative_binomial(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_90poisson(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_lam, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_92zipf(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_94geometric(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_96hypergeometric(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_ngood, PyObject *__pyx_v_nbad, PyObject *__pyx_v_nsample, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_98logseries(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_100multivariate_normal(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_cov, PyObject *__pyx_v_size, PyObject *__pyx_v_method); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_102multinomial(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, npy_intp __pyx_v_n, PyObject *__pyx_v_pvals, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_104dirichlet(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_alpha, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_106shuffle(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
static PyObject *__pyx_pf_11randomstate_13mlfg_1279_861_11RandomState_108permutation(struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* 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 PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* 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 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 */
static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
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 */
static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
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 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_tp_new_11randomstate_13mlfg_1279_861_RandomState(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_float_0_;
static PyObject *__pyx_float_0_0;
static PyObject *__pyx_float_1_0;
static PyObject *__pyx_int_0;
static PyObject *__pyx_int_1;
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static PyObject *__pyx_int_32;
static PyObject *__pyx_int_128;
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static PyObject *__pyx_int_2147483648;
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static PyObject *__pyx_int_9223372036854775808;
static PyObject *__pyx_int_18446744073709551615;
static PyObject *__pyx_int_18446744073709551616;
static PyObject *__pyx_int_neg_1;
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static PyObject *__pyx_int_neg_2147483648;
static PyObject *__pyx_int_neg_9223372036854775808;
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        __pyx_t_2 = __pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_d(__pyx_v_state, __pyx_v_func, __pyx_v_size, __pyx_v_lock, __pyx_v_a_arr, __pyx_v_a_name, __pyx_v_a_constraint); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 502, __pyx_L1_error)
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        goto __pyx_L0;

        /* "randomstate/array_utilities.pxi":501
 *     if not is_scalar:
 *         if narg_long == 0:
 *             if narg_double == 1:             # <<<<<<<<<<<<<<
 *                 return discrete_broadcast_d(state, func, size, lock,
 *                                             a_arr, a_name, a_constraint)
 */
      }

      /* "randomstate/array_utilities.pxi":504
 *                 return discrete_broadcast_d(state, func, size, lock,
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 2:             # <<<<<<<<<<<<<<
 *                 return discrete_broadcast_dd(state, func, size, lock,
 *                                              a_arr, a_name, a_constraint,
 */
      __pyx_t_1 = ((__pyx_v_narg_double == 2) != 0);
      if (__pyx_t_1) {

        /* "randomstate/array_utilities.pxi":505
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 2:
 *                 return discrete_broadcast_dd(state, func, size, lock,             # <<<<<<<<<<<<<<
 *                                              a_arr, a_name, a_constraint,
 *                                              b_arr, b_name, b_constraint)
 */
        __Pyx_XDECREF(__pyx_r);

        /* "randomstate/array_utilities.pxi":506
 *             elif narg_double == 2:
 *                 return discrete_broadcast_dd(state, func, size, lock,
 *                                              a_arr, a_name, a_constraint,             # <<<<<<<<<<<<<<
 *                                              b_arr, b_name, b_constraint)
 *         elif narg_long == 1:
 */
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        /* "randomstate/array_utilities.pxi":507
 *                 return discrete_broadcast_dd(state, func, size, lock,
 *                                              a_arr, a_name, a_constraint,
 *                                              b_arr, b_name, b_constraint)             # <<<<<<<<<<<<<<
 *         elif narg_long == 1:
 *             if narg_double == 0:
 */
        if (unlikely(!__pyx_v_b_arr)) { __Pyx_RaiseUnboundLocalError("b_arr"); __PYX_ERR(1, 507, __pyx_L1_error) }

        /* "randomstate/array_utilities.pxi":505
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 2:
 *                 return discrete_broadcast_dd(state, func, size, lock,             # <<<<<<<<<<<<<<
 *                                              a_arr, a_name, a_constraint,
 *                                              b_arr, b_name, b_constraint)
 */
        __pyx_t_2 = __pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_dd(__pyx_v_state, __pyx_v_func, __pyx_v_size, __pyx_v_lock, __pyx_v_a_arr, __pyx_v_a_name, __pyx_v_a_constraint, __pyx_v_b_arr, __pyx_v_b_name, __pyx_v_b_constraint); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 505, __pyx_L1_error)
        __Pyx_GOTREF(__pyx_t_2);
        __pyx_r = __pyx_t_2;
        __pyx_t_2 = 0;
        goto __pyx_L0;

        /* "randomstate/array_utilities.pxi":504
 *                 return discrete_broadcast_d(state, func, size, lock,
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 2:             # <<<<<<<<<<<<<<
 *                 return discrete_broadcast_dd(state, func, size, lock,
 *                                              a_arr, a_name, a_constraint,
 */
      }

      /* "randomstate/array_utilities.pxi":500
 * 
 *     if not is_scalar:
 *         if narg_long == 0:             # <<<<<<<<<<<<<<
 *             if narg_double == 1:
 *                 return discrete_broadcast_d(state, func, size, lock,
 */
      break;

      /* "randomstate/array_utilities.pxi":508
 *                                              a_arr, a_name, a_constraint,
 *                                              b_arr, b_name, b_constraint)
 *         elif narg_long == 1:             # <<<<<<<<<<<<<<
 *             if narg_double == 0:
 *                 return discrete_broadcast_i(state, func, size, lock,
 */
      case 1:

      /* "randomstate/array_utilities.pxi":509
 *                                              b_arr, b_name, b_constraint)
 *         elif narg_long == 1:
 *             if narg_double == 0:             # <<<<<<<<<<<<<<
 *                 return discrete_broadcast_i(state, func, size, lock,
 *                                             a_arr, a_name, a_constraint)
 */
      __pyx_t_1 = ((__pyx_v_narg_double == 0) != 0);
      if (__pyx_t_1) {

        /* "randomstate/array_utilities.pxi":510
 *         elif narg_long == 1:
 *             if narg_double == 0:
 *                 return discrete_broadcast_i(state, func, size, lock,             # <<<<<<<<<<<<<<
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 1:
 */
        __Pyx_XDECREF(__pyx_r);

        /* "randomstate/array_utilities.pxi":511
 *             if narg_double == 0:
 *                 return discrete_broadcast_i(state, func, size, lock,
 *                                             a_arr, a_name, a_constraint)             # <<<<<<<<<<<<<<
 *             elif narg_double == 1:
 *                 return discrete_broadcast_di(state, func, size, lock,
 */
        if (unlikely(!__pyx_v_a_arr)) { __Pyx_RaiseUnboundLocalError("a_arr"); __PYX_ERR(1, 511, __pyx_L1_error) }

        /* "randomstate/array_utilities.pxi":510
 *         elif narg_long == 1:
 *             if narg_double == 0:
 *                 return discrete_broadcast_i(state, func, size, lock,             # <<<<<<<<<<<<<<
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 1:
 */
        __pyx_t_2 = __pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_i(__pyx_v_state, __pyx_v_func, __pyx_v_size, __pyx_v_lock, __pyx_v_a_arr, __pyx_v_a_name, __pyx_v_a_constraint); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 510, __pyx_L1_error)
        __Pyx_GOTREF(__pyx_t_2);
        __pyx_r = __pyx_t_2;
        __pyx_t_2 = 0;
        goto __pyx_L0;

        /* "randomstate/array_utilities.pxi":509
 *                                              b_arr, b_name, b_constraint)
 *         elif narg_long == 1:
 *             if narg_double == 0:             # <<<<<<<<<<<<<<
 *                 return discrete_broadcast_i(state, func, size, lock,
 *                                             a_arr, a_name, a_constraint)
 */
      }

      /* "randomstate/array_utilities.pxi":512
 *                 return discrete_broadcast_i(state, func, size, lock,
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 1:             # <<<<<<<<<<<<<<
 *                 return discrete_broadcast_di(state, func, size, lock,
 *                                              a_arr, a_name, a_constraint,
 */
      __pyx_t_1 = ((__pyx_v_narg_double == 1) != 0);
      if (__pyx_t_1) {

        /* "randomstate/array_utilities.pxi":513
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 1:
 *                 return discrete_broadcast_di(state, func, size, lock,             # <<<<<<<<<<<<<<
 *                                              a_arr, a_name, a_constraint,
 *                                              b_arr, b_name, b_constraint)
 */
        __Pyx_XDECREF(__pyx_r);

        /* "randomstate/array_utilities.pxi":514
 *             elif narg_double == 1:
 *                 return discrete_broadcast_di(state, func, size, lock,
 *                                              a_arr, a_name, a_constraint,             # <<<<<<<<<<<<<<
 *                                              b_arr, b_name, b_constraint)
 *         else:
 */
        if (unlikely(!__pyx_v_a_arr)) { __Pyx_RaiseUnboundLocalError("a_arr"); __PYX_ERR(1, 514, __pyx_L1_error) }

        /* "randomstate/array_utilities.pxi":515
 *                 return discrete_broadcast_di(state, func, size, lock,
 *                                              a_arr, a_name, a_constraint,
 *                                              b_arr, b_name, b_constraint)             # <<<<<<<<<<<<<<
 *         else:
 *             raise NotImplementedError("No vector path available")
 */
        if (unlikely(!__pyx_v_b_arr)) { __Pyx_RaiseUnboundLocalError("b_arr"); __PYX_ERR(1, 515, __pyx_L1_error) }

        /* "randomstate/array_utilities.pxi":513
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 1:
 *                 return discrete_broadcast_di(state, func, size, lock,             # <<<<<<<<<<<<<<
 *                                              a_arr, a_name, a_constraint,
 *                                              b_arr, b_name, b_constraint)
 */
        __pyx_t_2 = __pyx_f_11randomstate_13mlfg_1279_861_discrete_broadcast_di(__pyx_v_state, __pyx_v_func, __pyx_v_size, __pyx_v_lock, __pyx_v_a_arr, __pyx_v_a_name, __pyx_v_a_constraint, __pyx_v_b_arr, __pyx_v_b_name, __pyx_v_b_constraint); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 513, __pyx_L1_error)
        __Pyx_GOTREF(__pyx_t_2);
        __pyx_r = __pyx_t_2;
        __pyx_t_2 = 0;
        goto __pyx_L0;

        /* "randomstate/array_utilities.pxi":512
 *                 return discrete_broadcast_i(state, func, size, lock,
 *                                             a_arr, a_name, a_constraint)
 *             elif narg_double == 1:             # <<<<<<<<<<<<<<
 *                 return discrete_broadcast_di(state, func, size, lock,
 *                                              a_arr, a_name, a_constraint,
 */
      }

      /* "randomstate/array_utilities.pxi":508
 *                                              a_arr, a_name, a_constraint,
 *                                              b_arr, b_name, b_constraint)
 *         elif narg_long == 1:             # <<<<<<<<<<<<<<
 *             if narg_double == 0:
 *                 return discrete_broadcast_i(state, func, size, lock,
 */
      break;
      default:

      /* "randomstate/array_utilities.pxi":517
 *                                              b_arr, b_name, b_constraint)
 *         else:
 *             raise NotImplementedError("No vector path available")             # <<<<<<<<<<<<<<
 * 
 * 
 */
      __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_NotImplementedError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 517, __pyx_L1_error)
      __Pyx_GOTREF(__pyx_t_2);
      __Pyx_Raise(__pyx_t_2, 0, 0, 0);
      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
      __PYX_ERR(1, 517, __pyx_L1_error)
      break;
    }

    /* "randomstate/array_utilities.pxi":499
 *             is_scalar = is_scalar and np.PyArray_NDIM(c_arr) == 0
 * 
 *     if not is_scalar:             # <<<<<<<<<<<<<<
 *         if narg_long == 0:
 *             if narg_double == 1:
 */
  }

  /* "randomstate/array_utilities.pxi":520
 * 
 * 
 *     if narg_double > 0:             # <<<<<<<<<<<<<<
 *         _da = PyFloat_AsDouble(a)
 *         if a_constraint != CONS_NONE and is_scalar:
 */
  __pyx_t_1 = ((__pyx_v_narg_double > 0) != 0);
  if (__pyx_t_1) {

    /* "randomstate/array_utilities.pxi":521
 * 
 *     if narg_double > 0:
 *         _da = PyFloat_AsDouble(a)             # <<<<<<<<<<<<<<
 *         if a_constraint != CONS_NONE and is_scalar:
 *             check_constraint(_da, a_name, a_constraint)
 */
    __pyx_v__da = PyFloat_AsDouble(__pyx_v_a);

    /* "randomstate/array_utilities.pxi":522
 *     if narg_double > 0:
 *         _da = PyFloat_AsDouble(a)
 *         if a_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *             check_constraint(_da, a_name, a_constraint)
 * 
 */
    __pyx_t_4 = ((__pyx_v_a_constraint != __pyx_e_11randomstate_13mlfg_1279_861_CONS_NONE) != 0);
    if (__pyx_t_4) {
    } else {
      __pyx_t_1 = __pyx_t_4;
      goto __pyx_L25_bool_binop_done;
    }
    __pyx_t_4 = (__pyx_v_is_scalar != 0);
    __pyx_t_1 = __pyx_t_4;
    __pyx_L25_bool_binop_done:;
    if (__pyx_t_1) {

      /* "randomstate/array_utilities.pxi":523
 *         _da = PyFloat_AsDouble(a)
 *         if a_constraint != CONS_NONE and is_scalar:
 *             check_constraint(_da, a_name, a_constraint)             # <<<<<<<<<<<<<<
 * 
 *         if narg_double > 1:
 */
      __pyx_t_5 = __pyx_f_11randomstate_13mlfg_1279_861_check_constraint(__pyx_v__da, __pyx_v_a_name, __pyx_v_a_constraint); if (unlikely(__pyx_t_5 == -1)) __PYX_ERR(1, 523, __pyx_L1_error)

      /* "randomstate/array_utilities.pxi":522
 *     if narg_double > 0:
 *         _da = PyFloat_AsDouble(a)
 *         if a_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *             check_constraint(_da, a_name, a_constraint)
 * 
 */
    }

    /* "randomstate/array_utilities.pxi":525
 *             check_constraint(_da, a_name, a_constraint)
 * 
 *         if narg_double > 1:             # <<<<<<<<<<<<<<
 *             _db = PyFloat_AsDouble(b)
 *             if b_constraint != CONS_NONE and is_scalar:
 */
    __pyx_t_1 = ((__pyx_v_narg_double > 1) != 0);
    if (__pyx_t_1) {

      /* "randomstate/array_utilities.pxi":526
 * 
 *         if narg_double > 1:
 *             _db = PyFloat_AsDouble(b)             # <<<<<<<<<<<<<<
 *             if b_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(_db, b_name, b_constraint)
 */
      __pyx_v__db = PyFloat_AsDouble(__pyx_v_b);

      /* "randomstate/array_utilities.pxi":527
 *         if narg_double > 1:
 *             _db = PyFloat_AsDouble(b)
 *             if b_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *                 check_constraint(_db, b_name, b_constraint)
 *         elif narg_long == 1:
 */
      __pyx_t_4 = ((__pyx_v_b_constraint != __pyx_e_11randomstate_13mlfg_1279_861_CONS_NONE) != 0);
      if (__pyx_t_4) {
      } else {
        __pyx_t_1 = __pyx_t_4;
        goto __pyx_L29_bool_binop_done;
      }
      __pyx_t_4 = (__pyx_v_is_scalar != 0);
      __pyx_t_1 = __pyx_t_4;
      __pyx_L29_bool_binop_done:;
      if (__pyx_t_1) {

        /* "randomstate/array_utilities.pxi":528
 *             _db = PyFloat_AsDouble(b)
 *             if b_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(_db, b_name, b_constraint)             # <<<<<<<<<<<<<<
 *         elif narg_long == 1:
 *             _ib = PyInt_AsLong(b)
 */
        __pyx_t_5 = __pyx_f_11randomstate_13mlfg_1279_861_check_constraint(__pyx_v__db, __pyx_v_b_name, __pyx_v_b_constraint); if (unlikely(__pyx_t_5 == -1)) __PYX_ERR(1, 528, __pyx_L1_error)

        /* "randomstate/array_utilities.pxi":527
 *         if narg_double > 1:
 *             _db = PyFloat_AsDouble(b)
 *             if b_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *                 check_constraint(_db, b_name, b_constraint)
 *         elif narg_long == 1:
 */
      }

      /* "randomstate/array_utilities.pxi":525
 *             check_constraint(_da, a_name, a_constraint)
 * 
 *         if narg_double > 1:             # <<<<<<<<<<<<<<
 *             _db = PyFloat_AsDouble(b)
 *             if b_constraint != CONS_NONE and is_scalar:
 */
      goto __pyx_L27;
    }

    /* "randomstate/array_utilities.pxi":529
 *             if b_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(_db, b_name, b_constraint)
 *         elif narg_long == 1:             # <<<<<<<<<<<<<<
 *             _ib = PyInt_AsLong(b)
 *             if b_constraint != CONS_NONE and is_scalar:
 */
    __pyx_t_1 = ((__pyx_v_narg_long == 1) != 0);
    if (__pyx_t_1) {

      /* "randomstate/array_utilities.pxi":530
 *                 check_constraint(_db, b_name, b_constraint)
 *         elif narg_long == 1:
 *             _ib = PyInt_AsLong(b)             # <<<<<<<<<<<<<<
 *             if b_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(<double>_ib, b_name, b_constraint)
 */
      __pyx_v__ib = PyInt_AsLong(__pyx_v_b);

      /* "randomstate/array_utilities.pxi":531
 *         elif narg_long == 1:
 *             _ib = PyInt_AsLong(b)
 *             if b_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *                 check_constraint(<double>_ib, b_name, b_constraint)
 *     else:
 */
      __pyx_t_4 = ((__pyx_v_b_constraint != __pyx_e_11randomstate_13mlfg_1279_861_CONS_NONE) != 0);
      if (__pyx_t_4) {
      } else {
        __pyx_t_1 = __pyx_t_4;
        goto __pyx_L32_bool_binop_done;
      }
      __pyx_t_4 = (__pyx_v_is_scalar != 0);
      __pyx_t_1 = __pyx_t_4;
      __pyx_L32_bool_binop_done:;
      if (__pyx_t_1) {

        /* "randomstate/array_utilities.pxi":532
 *             _ib = PyInt_AsLong(b)
 *             if b_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(<double>_ib, b_name, b_constraint)             # <<<<<<<<<<<<<<
 *     else:
 *         if narg_long > 0:
 */
        __pyx_t_5 = __pyx_f_11randomstate_13mlfg_1279_861_check_constraint(((double)__pyx_v__ib), __pyx_v_b_name, __pyx_v_b_constraint); if (unlikely(__pyx_t_5 == -1)) __PYX_ERR(1, 532, __pyx_L1_error)

        /* "randomstate/array_utilities.pxi":531
 *         elif narg_long == 1:
 *             _ib = PyInt_AsLong(b)
 *             if b_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *                 check_constraint(<double>_ib, b_name, b_constraint)
 *     else:
 */
      }

      /* "randomstate/array_utilities.pxi":529
 *             if b_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(_db, b_name, b_constraint)
 *         elif narg_long == 1:             # <<<<<<<<<<<<<<
 *             _ib = PyInt_AsLong(b)
 *             if b_constraint != CONS_NONE and is_scalar:
 */
    }
    __pyx_L27:;

    /* "randomstate/array_utilities.pxi":520
 * 
 * 
 *     if narg_double > 0:             # <<<<<<<<<<<<<<
 *         _da = PyFloat_AsDouble(a)
 *         if a_constraint != CONS_NONE and is_scalar:
 */
    goto __pyx_L23;
  }

  /* "randomstate/array_utilities.pxi":534
 *                 check_constraint(<double>_ib, b_name, b_constraint)
 *     else:
 *         if narg_long > 0:             # <<<<<<<<<<<<<<
 *             _ia = PyInt_AsLong(a)
 *             if a_constraint != CONS_NONE and is_scalar:
 */
  /*else*/ {
    __pyx_t_1 = ((__pyx_v_narg_long > 0) != 0);
    if (__pyx_t_1) {

      /* "randomstate/array_utilities.pxi":535
 *     else:
 *         if narg_long > 0:
 *             _ia = PyInt_AsLong(a)             # <<<<<<<<<<<<<<
 *             if a_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(<double>_ia, a_name, a_constraint)
 */
      __pyx_v__ia = PyInt_AsLong(__pyx_v_a);

      /* "randomstate/array_utilities.pxi":536
 *         if narg_long > 0:
 *             _ia = PyInt_AsLong(a)
 *             if a_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *                 check_constraint(<double>_ia, a_name, a_constraint)
 *         if narg_long > 1:
 */
      __pyx_t_4 = ((__pyx_v_a_constraint != __pyx_e_11randomstate_13mlfg_1279_861_CONS_NONE) != 0);
      if (__pyx_t_4) {
      } else {
        __pyx_t_1 = __pyx_t_4;
        goto __pyx_L36_bool_binop_done;
      }
      __pyx_t_4 = (__pyx_v_is_scalar != 0);
      __pyx_t_1 = __pyx_t_4;
      __pyx_L36_bool_binop_done:;
      if (__pyx_t_1) {

        /* "randomstate/array_utilities.pxi":537
 *             _ia = PyInt_AsLong(a)
 *             if a_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(<double>_ia, a_name, a_constraint)             # <<<<<<<<<<<<<<
 *         if narg_long > 1:
 *             _ib = PyInt_AsLong(b)
 */
        __pyx_t_5 = __pyx_f_11randomstate_13mlfg_1279_861_check_constraint(((double)__pyx_v__ia), __pyx_v_a_name, __pyx_v_a_constraint); if (unlikely(__pyx_t_5 == -1)) __PYX_ERR(1, 537, __pyx_L1_error)

        /* "randomstate/array_utilities.pxi":536
 *         if narg_long > 0:
 *             _ia = PyInt_AsLong(a)
 *             if a_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *                 check_constraint(<double>_ia, a_name, a_constraint)
 *         if narg_long > 1:
 */
      }

      /* "randomstate/array_utilities.pxi":534
 *                 check_constraint(<double>_ib, b_name, b_constraint)
 *     else:
 *         if narg_long > 0:             # <<<<<<<<<<<<<<
 *             _ia = PyInt_AsLong(a)
 *             if a_constraint != CONS_NONE and is_scalar:
 */
    }

    /* "randomstate/array_utilities.pxi":538
 *             if a_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(<double>_ia, a_name, a_constraint)
 *         if narg_long > 1:             # <<<<<<<<<<<<<<
 *             _ib = PyInt_AsLong(b)
 *             if b_constraint != CONS_NONE and is_scalar:
 */
    __pyx_t_1 = ((__pyx_v_narg_long > 1) != 0);
    if (__pyx_t_1) {

      /* "randomstate/array_utilities.pxi":539
 *                 check_constraint(<double>_ia, a_name, a_constraint)
 *         if narg_long > 1:
 *             _ib = PyInt_AsLong(b)             # <<<<<<<<<<<<<<
 *             if b_constraint != CONS_NONE and is_scalar:
 *                 check_constraint(<double>_ib, b_name, b_constraint)
 */
      __pyx_v__ib = PyInt_AsLong(__pyx_v_b);

      /* "randomstate/array_utilities.pxi":540
 *         if narg_long > 1:
 *             _ib = PyInt_AsLong(b)
 *             if b_constraint != CONS_NONE and is_scalar:             # <<<<<<<<<<<<<<
 *                 check_constraint(<double>_ib, b_name, b_constraint)
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static char __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_96hypergeometric[] = "\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.\n        nbad : int or array_like of ints\n            Number of ways to make a bad selection.  Must be nonnegative.\n        nsample : int or array_like of ints\n            Number of items sampled.  Must be at least 1 and at most\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.\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them""\n        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        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               http://en.wikipedia.org/wiki/Hypergeometric_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\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 = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
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 * 
 *         if start < 0:
 */
    goto __pyx_L3;
  }

  /* "View.MemoryView":821
 *     else:
 * 
 *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<
 * 
 *         if have_step and step == 0:
 */
  /*else*/ {
    __pyx_t_1 = ((__pyx_v_have_step != 0) != 0);
    if (__pyx_t_1) {
    } else {
      __pyx_t_2 = __pyx_t_1;
      goto __pyx_L6_bool_binop_done;
    }
    __pyx_t_1 = ((__pyx_v_step < 0) != 0);
    __pyx_t_2 = __pyx_t_1;
    __pyx_L6_bool_binop_done:;
    __pyx_v_negative_step = __pyx_t_2;

    /* "View.MemoryView":823
 *         negative_step = have_step != 0 and step < 0
 * 
 *         if have_step and step == 0:             # <<<<<<<<<<<<<<
 *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
 * 
 */
    __pyx_t_1 = (__pyx_v_have_step != 0);
    if (__pyx_t_1) {
    } else {
      __pyx_t_2 = __pyx_t_1;
      goto __pyx_L9_bool_binop_done;
    }
    __pyx_t_1 = ((__pyx_v_step == 0) != 0);
    __pyx_t_2 = __pyx_t_1;
    __pyx_L9_bool_binop_done:;
    if (__pyx_t_2) {

      /* "View.MemoryView":824
 * 
 *         if have_step and step == 0:
 *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
 * 
 * 
 */
      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) __PYX_ERR(6, 824, __pyx_L1_error)

      /* "View.MemoryView":823
 *         negative_step = have_step != 0 and step < 0
 * 
 *         if have_step and step == 0:             # <<<<<<<<<<<<<<
 *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
 * 
 */
    }

    /* "View.MemoryView":827
 * 
 * 
 *         if have_start:             # <<<<<<<<<<<<<<
 *             if start < 0:
 *                 start += shape
 */
    __pyx_t_2 = (__pyx_v_have_start != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":828
 * 
 *         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":829
 *         if have_start:
 *             if start < 0:
 *                 start += shape             # <<<<<<<<<<<<<<
 *                 if start < 0:
 *                     start = 0
 */
        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);

        /* "View.MemoryView":830
 *             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":831
 *                 start += shape
 *                 if start < 0:
 *                     start = 0             # <<<<<<<<<<<<<<
 *             elif start >= shape:
 *                 if negative_step:
 */
          __pyx_v_start = 0;

          /* "View.MemoryView":830
 *             if start < 0:
 *                 start += shape
 *                 if start < 0:             # <<<<<<<<<<<<<<
 *                     start = 0
 *             elif start >= shape:
 */
        }

        /* "View.MemoryView":828
 * 
 *         if have_start:
 *             if start < 0:             # <<<<<<<<<<<<<<
 *                 start += shape
 *                 if start < 0:
 */
        goto __pyx_L12;
      }

      /* "View.MemoryView":832
 *                 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":833
 *                     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":834
 *             elif start >= shape:
 *                 if negative_step:
 *                     start = shape - 1             # <<<<<<<<<<<<<<
 *                 else:
 *                     start = shape
 */
          __pyx_v_start = (__pyx_v_shape - 1);

          /* "View.MemoryView":833
 *                     start = 0
 *             elif start >= shape:
 *                 if negative_step:             # <<<<<<<<<<<<<<
 *                     start = shape - 1
 *                 else:
 */
          goto __pyx_L14;
        }

        /* "View.MemoryView":836
 *                     start = shape - 1
 *                 else:
 *                     start = shape             # <<<<<<<<<<<<<<
 *         else:
 *             if negative_step:
 */
        /*else*/ {
          __pyx_v_start = __pyx_v_shape;
        }
        __pyx_L14:;

        /* "View.MemoryView":832
 *                 if start < 0:
 *                     start = 0
 *             elif start >= shape:             # <<<<<<<<<<<<<<
 *                 if negative_step:
 *                     start = shape - 1
 */
      }
      __pyx_L12:;

      /* "View.MemoryView":827
 * 
 * 
 *         if have_start:             # <<<<<<<<<<<<<<
 *             if start < 0:
 *                 start += shape
 */
      goto __pyx_L11;
    }

    /* "View.MemoryView":838
 *                     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":839
 *         else:
 *             if negative_step:
 *                 start = shape - 1             # <<<<<<<<<<<<<<
 *             else:
 *                 start = 0
 */
        __pyx_v_start = (__pyx_v_shape - 1);

        /* "View.MemoryView":838
 *                     start = shape
 *         else:
 *             if negative_step:             # <<<<<<<<<<<<<<
 *                 start = shape - 1
 *             else:
 */
        goto __pyx_L15;
      }

      /* "View.MemoryView":841
 *                 start = shape - 1
 *             else:
 *                 start = 0             # <<<<<<<<<<<<<<
 * 
 *         if have_stop:
 */
      /*else*/ {
        __pyx_v_start = 0;
      }
      __pyx_L15:;
    }
    __pyx_L11:;

    /* "View.MemoryView":843
 *                 start = 0
 * 
 *         if have_stop:             # <<<<<<<<<<<<<<
 *             if stop < 0:
 *                 stop += shape
 */
    __pyx_t_2 = (__pyx_v_have_stop != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":844
 * 
 *         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":845
 *         if have_stop:
 *             if stop < 0:
 *                 stop += shape             # <<<<<<<<<<<<<<
 *                 if stop < 0:
 *                     stop = 0
 */
        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);

        /* "View.MemoryView":846
 *             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":847
 *                 stop += shape
 *                 if stop < 0:
 *                     stop = 0             # <<<<<<<<<<<<<<
 *             elif stop > shape:
 *                 stop = shape
 */
          __pyx_v_stop = 0;

          /* "View.MemoryView":846
 *             if stop < 0:
 *                 stop += shape
 *                 if stop < 0:             # <<<<<<<<<<<<<<
 *                     stop = 0
 *             elif stop > shape:
 */
        }

        /* "View.MemoryView":844
 * 
 *         if have_stop:
 *             if stop < 0:             # <<<<<<<<<<<<<<
 *                 stop += shape
 *                 if stop < 0:
 */
        goto __pyx_L17;
      }

      /* "View.MemoryView":848
 *                 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":849
 *                     stop = 0
 *             elif stop > shape:
 *                 stop = shape             # <<<<<<<<<<<<<<
 *         else:
 *             if negative_step:
 */
        __pyx_v_stop = __pyx_v_shape;

        /* "View.MemoryView":848
 *                 if stop < 0:
 *                     stop = 0
 *             elif stop > shape:             # <<<<<<<<<<<<<<
 *                 stop = shape
 *         else:
 */
      }
      __pyx_L17:;

      /* "View.MemoryView":843
 *                 start = 0
 * 
 *         if have_stop:             # <<<<<<<<<<<<<<
 *             if stop < 0:
 *                 stop += shape
 */
      goto __pyx_L16;
    }

    /* "View.MemoryView":851
 *                 stop = shape
 *         else:
 *             if negative_step:             # <<<<<<<<<<<<<<
 *                 stop = -1
 *             else:
 */
    /*else*/ {
      __pyx_t_2 = (__pyx_v_negative_step != 0);
      if (__pyx_t_2) {

        /* "View.MemoryView":852
 *         else:
 *             if negative_step:
 *                 stop = -1             # <<<<<<<<<<<<<<
 *             else:
 *                 stop = shape
 */
        __pyx_v_stop = -1L;

        /* "View.MemoryView":851
 *                 stop = shape
 *         else:
 *             if negative_step:             # <<<<<<<<<<<<<<
 *                 stop = -1
 *             else:
 */
        goto __pyx_L19;
      }

      /* "View.MemoryView":854
 *                 stop = -1
 *             else:
 *                 stop = shape             # <<<<<<<<<<<<<<
 * 
 *         if not have_step:
 */
      /*else*/ {
        __pyx_v_stop = __pyx_v_shape;
      }
      __pyx_L19:;
    }
    __pyx_L16:;

    /* "View.MemoryView":856
 *                 stop = shape
 * 
 *         if not have_step:             # <<<<<<<<<<<<<<
 *             step = 1
 * 
 */
    __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":857
 * 
 *         if not have_step:
 *             step = 1             # <<<<<<<<<<<<<<
 * 
 * 
 */
      __pyx_v_step = 1;

      /* "View.MemoryView":856
 *                 stop = shape
 * 
 *         if not have_step:             # <<<<<<<<<<<<<<
 *             step = 1
 * 
 */
    }

    /* "View.MemoryView":861
 * 
 *         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":863
 *             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":864
 * 
 *             if (stop - start) - step * new_shape:
 *                 new_shape += 1             # <<<<<<<<<<<<<<
 * 
 *         if new_shape < 0:
 */
      __pyx_v_new_shape = (__pyx_v_new_shape + 1);

      /* "View.MemoryView":863
 *             new_shape = (stop - start) // step
 * 
 *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
 *                 new_shape += 1
 * 
 */
    }

    /* "View.MemoryView":866
 *                 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":867
 * 
 *         if new_shape < 0:
 *             new_shape = 0             # <<<<<<<<<<<<<<
 * 
 * 
 */
      __pyx_v_new_shape = 0;

      /* "View.MemoryView":866
 *                 new_shape += 1
 * 
 *         if new_shape < 0:             # <<<<<<<<<<<<<<
 *             new_shape = 0
 * 
 */
    }

    /* "View.MemoryView":870
 * 
 * 
 *         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":871
 * 
 *         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":872
 *         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;
  }
  __pyx_L3:;

  /* "View.MemoryView":875
 * 
 * 
 *     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":876
 * 
 *     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":875
 * 
 * 
 *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
 *         dst.data += start * stride
 *     else:
 */
    goto __pyx_L23;
  }

  /* "View.MemoryView":878
 *         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":880
 *         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":881
 * 
 *     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":882
 *     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":883
 *         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":882
 *     if suboffset >= 0:
 *         if not is_slice:
 *             if new_ndim == 0:             # <<<<<<<<<<<<<<
 *                 dst.data = (<char **> dst.data)[0] + suboffset
 *             else:
 */
        goto __pyx_L26;
      }

      /* "View.MemoryView":885
 *                 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":886
 *             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 == -1)) __PYX_ERR(6, 885, __pyx_L1_error)
      }
      __pyx_L26:;

      /* "View.MemoryView":881
 * 
 *     if suboffset >= 0:
 *         if not is_slice:             # <<<<<<<<<<<<<<
 *             if new_ndim == 0:
 *                 dst.data = (<char **> dst.data)[0] + suboffset
 */
      goto __pyx_L25;
    }

    /* "View.MemoryView":888
 *                                      "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":880
 *         dst.suboffsets[suboffset_dim[0]] += start * stride
 * 
 *     if suboffset >= 0:             # <<<<<<<<<<<<<<
 *         if not is_slice:
 *             if new_ndim == 0:
 */
  }

  /* "View.MemoryView":890
 *             suboffset_dim[0] = new_ndim
 * 
 *     return 0             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_r = 0;
  goto __pyx_L0;

  /* "View.MemoryView":793
 * 
 * @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:;
  {
    #ifdef WITH_THREAD
    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
    #endif
    __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
    #ifdef WITH_THREAD
    PyGILState_Release(__pyx_gilstate_save);
    #endif
  }
  __pyx_r = -1;
  __pyx_L0:;
  return __pyx_r;
}

/* "View.MemoryView":896
 * 
 * @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;
  __Pyx_RefNannySetupContext("pybuffer_index", 0);

  /* "View.MemoryView":898
 * 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":899
 *                           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":902
 *     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":903
 * 
 *     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(6, 903, __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(6, 903, __pyx_L1_error)
    }
    __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize);

    /* "View.MemoryView":904
 *     if view.ndim == 0:
 *         shape = view.len / itemsize
 *         stride = itemsize             # <<<<<<<<<<<<<<
 *     else:
 *         shape = view.shape[dim]
 */
    __pyx_v_stride = __pyx_v_itemsize;

    /* "View.MemoryView":902
 *     cdef char *resultp
 * 
 *     if view.ndim == 0:             # <<<<<<<<<<<<<<
 *         shape = view.len / itemsize
 *         stride = itemsize
 */
    goto __pyx_L3;
  }

  /* "View.MemoryView":906
 *         stride = itemsize
 *     else:
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/* "View.MemoryView":1123
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    } 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":1137
 *     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":1136
 * 
 *     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":1138
 *        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):
 */
      memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent));

      /* "View.MemoryView":1136
 * 
 *     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":1140
 *            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;
      for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
        __pyx_v_i = __pyx_t_5;

        /* "View.MemoryView":1141
 *        else:
 *            for i in range(dst_extent):
 *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
 *                src_data += src_stride
 *                dst_data += dst_stride
 */
        memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize);

        /* "View.MemoryView":1142
 *            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":1143
 *                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":1135
 *     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":1145
 *                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;
    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
      __pyx_v_i = __pyx_t_5;

      /* "View.MemoryView":1146
 *     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":1150
 *                                      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":1151
 *                                      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":1123
 * 
 * @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":1153
 *             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":1156
 *                                   __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":1153
 *             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":1160
 * 
 * @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 int i
 */

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

  /* "View.MemoryView":1163
 *     "Return the size of the memory occupied by the slice in number of bytes"
 *     cdef int i
 *     cdef Py_ssize_t size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
 * 
 *     for i in range(ndim):
 */
  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
  __pyx_v_size = __pyx_t_1;

  /* "View.MemoryView":1165
 *     cdef Py_ssize_t size = src.memview.view.itemsize
 * 
 *     for i in range(ndim):             # <<<<<<<<<<<<<<
 *         size *= src.shape[i]
 * 
 */
  __pyx_t_2 = __pyx_v_ndim;
  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
    __pyx_v_i = __pyx_t_3;

    /* "View.MemoryView":1166
 * 
 *     for i in range(ndim):
 *         size *= src.shape[i]             # <<<<<<<<<<<<<<
 * 
 *     return size
 */
    __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i]));
  }

  /* "View.MemoryView":1168
 *         size *= src.shape[i]
 * 
 *     return size             # <<<<<<<<<<<<<<
 * 
 * @cname('__pyx_fill_contig_strides_array')
 */
  __pyx_r = __pyx_v_size;
  goto __pyx_L0;

  /* "View.MemoryView":1160
 * 
 * @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 int i
 */

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

/* "View.MemoryView":1171
 * 
 * @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;

  /* "View.MemoryView":1180
 *     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":1181
 * 
 *     if order == 'F':
 *         for idx in range(ndim):             # <<<<<<<<<<<<<<
 *             strides[idx] = stride
 *             stride = stride * shape[idx]
 */
    __pyx_t_2 = __pyx_v_ndim;
    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
      __pyx_v_idx = __pyx_t_3;

      /* "View.MemoryView":1182
 *     if order == 'F':
 *         for idx in range(ndim):
 *             strides[idx] = stride             # <<<<<<<<<<<<<<
 *             stride = stride * shape[idx]
 *     else:
 */
      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;

      /* "View.MemoryView":1183
 *         for idx in range(ndim):
 *             strides[idx] = stride
 *             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":1180
 *     cdef int idx
 * 
 *     if order == 'F':             # <<<<<<<<<<<<<<
 *         for idx in range(ndim):
 *             strides[idx] = stride
 */
    goto __pyx_L3;
  }

  /* "View.MemoryView":1185
 *             stride = stride * shape[idx]
 *     else:
 *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
 *             strides[idx] = stride
 *             stride = stride * shape[idx]
 */
  /*else*/ {
    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1L; __pyx_t_2-=1) {
      __pyx_v_idx = __pyx_t_2;

      /* "View.MemoryView":1186
 *     else:
 *         for idx in range(ndim - 1, -1, -1):
 *             strides[idx] = stride             # <<<<<<<<<<<<<<
 *             stride = stride * shape[idx]
 * 
 */
      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;

      /* "View.MemoryView":1187
 *         for idx in range(ndim - 1, -1, -1):
 *             strides[idx] = stride
 *             stride = stride * shape[idx]             # <<<<<<<<<<<<<<
 * 
 *     return stride
 */
      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
    }
  }
  __pyx_L3:;

  /* "View.MemoryView":1189
 *             stride = stride * shape[idx]
 * 
 *     return stride             # <<<<<<<<<<<<<<
 * 
 * @cname('__pyx_memoryview_copy_data_to_temp')
 */
  __pyx_r = __pyx_v_stride;
  goto __pyx_L0;

  /* "View.MemoryView":1171
 * 
 * @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":1192
 * 
 * @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;

  /* "View.MemoryView":1203
 *     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":1204
 * 
 *     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":1206
 *     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":1207
 * 
 *     result = malloc(size)
 *     if not result:             # <<<<<<<<<<<<<<
 *         _err(MemoryError, NULL)
 * 
 */
  __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1208
 *     result = malloc(size)
 *     if not result:
 *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<
 * 
 * 
 */
    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == -1)) __PYX_ERR(6, 1208, __pyx_L1_error)

    /* "View.MemoryView":1207
 * 
 *     result = malloc(size)
 *     if not result:             # <<<<<<<<<<<<<<
 *         _err(MemoryError, NULL)
 * 
 */
  }

  /* "View.MemoryView":1211
 * 
 * 
 *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<
 *     tmpslice.memview = src.memview
 *     for i in range(ndim):
 */
  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);

  /* "View.MemoryView":1212
 * 
 *     tmpslice.data = <char *> result
 *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<
 *     for i in range(ndim):
 *         tmpslice.shape[i] = src.shape[i]
 */
  __pyx_t_4 = __pyx_v_src->memview;
  __pyx_v_tmpslice->memview = __pyx_t_4;

  /* "View.MemoryView":1213
 *     tmpslice.data = <char *> result
 *     tmpslice.memview = src.memview
 *     for i in range(ndim):             # <<<<<<<<<<<<<<
 *         tmpslice.shape[i] = src.shape[i]
 *         tmpslice.suboffsets[i] = -1
 */
  __pyx_t_3 = __pyx_v_ndim;
  for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) {
    __pyx_v_i = __pyx_t_5;

    /* "View.MemoryView":1214
 *     tmpslice.memview = src.memview
 *     for i in range(ndim):
 *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<
 *         tmpslice.suboffsets[i] = -1
 * 
 */
    (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);

    /* "View.MemoryView":1215
 *     for i in range(ndim):
 *         tmpslice.shape[i] = src.shape[i]
 *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
 * 
 *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,
 */
    (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L;
  }

  /* "View.MemoryView":1217
 *         tmpslice.suboffsets[i] = -1
 * 
 *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,             # <<<<<<<<<<<<<<
 *                               ndim, order)
 * 
 */
  __pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order);

  /* "View.MemoryView":1221
 * 
 * 
 *     for i in range(ndim):             # <<<<<<<<<<<<<<
 *         if tmpslice.shape[i] == 1:
 *             tmpslice.strides[i] = 0
 */
  __pyx_t_3 = __pyx_v_ndim;
  for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) {
    __pyx_v_i = __pyx_t_5;

    /* "View.MemoryView":1222
 * 
 *     for i in range(ndim):
 *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
 *             tmpslice.strides[i] = 0
 * 
 */
    __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0);
    if (__pyx_t_2) {

      /* "View.MemoryView":1223
 *     for i in range(ndim):
 *         if tmpslice.shape[i] == 1:
 *             tmpslice.strides[i] = 0             # <<<<<<<<<<<<<<
 * 
 *     if slice_is_contig(src[0], order, ndim):
 */
      (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0;

      /* "View.MemoryView":1222
 * 
 *     for i in range(ndim):
 *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
 *             tmpslice.strides[i] = 0
 * 
 */
    }
  }

  /* "View.MemoryView":1225
 *             tmpslice.strides[i] = 0
 * 
 *     if slice_is_contig(src[0], order, ndim):             # <<<<<<<<<<<<<<
 *         memcpy(result, src.data, size)
 *     else:
 */
  __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1226
 * 
 *     if slice_is_contig(src[0], order, ndim):
 *         memcpy(result, src.data, size)             # <<<<<<<<<<<<<<
 *     else:
 *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
 */
    memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size);

    /* "View.MemoryView":1225
 *             tmpslice.strides[i] = 0
 * 
 *     if slice_is_contig(src[0], order, ndim):             # <<<<<<<<<<<<<<
 *         memcpy(result, src.data, size)
 *     else:
 */
    goto __pyx_L9;
  }

  /* "View.MemoryView":1228
 *         memcpy(result, src.data, size)
 *     else:
 *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)             # <<<<<<<<<<<<<<
 * 
 *     return result
 */
  /*else*/ {
    copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize);
  }
  __pyx_L9:;

  /* "View.MemoryView":1230
 *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
 * 
 *     return result             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_r = __pyx_v_result;
  goto __pyx_L0;

  /* "View.MemoryView":1192
 * 
 * @cname('__pyx_memoryview_copy_data_to_temp')
 * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
 *                              __Pyx_memviewslice *tmpslice,
 *                              char order,
 */

  /* function exit code */
  __pyx_L1_error:;
  {
    #ifdef WITH_THREAD
    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
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 *                 broadcasting = True             # <<<<<<<<<<<<<<
 *                 src.strides[i] = 0
 *             else:
 */
        __pyx_v_broadcasting = 1;

        /* "View.MemoryView":1279
 *             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":1277
 *     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":1281
 *                 src.strides[i] = 0
 *             else:
 *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
 * 
 *         if src.suboffsets[i] >= 0:
 */
      /*else*/ {
        __pyx_t_4 = __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_4 == -1)) __PYX_ERR(6, 1281, __pyx_L1_error)
      }
      __pyx_L7:;

      /* "View.MemoryView":1276
 * 
 *     for i in range(ndim):
 *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
 *             if src.shape[i] == 1:
 *                 broadcasting = True
 */
    }

    /* "View.MemoryView":1283
 *                 _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":1284
 * 
 *         if src.suboffsets[i] >= 0:
 *             _err_dim(ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
 * 
 *     if slices_overlap(&src, &dst, ndim, itemsize):
 */
      __pyx_t_4 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_4 == -1)) __PYX_ERR(6, 1284, __pyx_L1_error)

      /* "View.MemoryView":1283
 *                 _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":1286
 *             _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":1288
 *     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":1289
 * 
 *         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":1288
 *     if slices_overlap(&src, &dst, ndim, itemsize):
 * 
 *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
 *             order = get_best_order(&dst, ndim)
 * 
 */
    }

    /* "View.MemoryView":1291
 *             order = get_best_order(&dst, ndim)
 * 
 *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
 *         src = tmp
 * 
 */
    __pyx_t_6 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_6 == NULL)) __PYX_ERR(6, 1291, __pyx_L1_error)
    __pyx_v_tmpdata = __pyx_t_6;

    /* "View.MemoryView":1292
 * 
 *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
 *         src = tmp             # <<<<<<<<<<<<<<
 * 
 *     if not broadcasting:
 */
    __pyx_v_src = __pyx_v_tmp;

    /* "View.MemoryView":1286
 *             _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":1294
 *         src = tmp
 * 
 *     if not broadcasting:             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
  if (__pyx_t_2) {

    /* "View.MemoryView":1297
 * 
 * 
 *         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":1298
 * 
 *         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":1297
 * 
 * 
 *         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":1299
 *         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":1300
 *             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":1299
 *         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":1302
 *             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":1304
 *         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":1305
 * 
 *             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)
 */
      memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim));

      /* "View.MemoryView":1306
 *             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":1307
 *             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":1308
 *             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":1302
 *             direct_copy = slice_is_contig(dst, 'F', ndim)
 * 
 *         if direct_copy:             # <<<<<<<<<<<<<<
 * 
 *             refcount_copying(&dst, dtype_is_object, ndim, False)
 */
    }

    /* "View.MemoryView":1294
 *         src = tmp
 * 
 *     if not broadcasting:             # <<<<<<<<<<<<<<
 * 
 * 
 */
  }

  /* "View.MemoryView":1310
 *             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_7 = (__pyx_t_2 != 0);
  if (__pyx_t_7) {

    /* "View.MemoryView":1313
 * 
 * 
 *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
 *         transpose_memslice(&dst)
 * 
 */
    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == 0)) __PYX_ERR(6, 1313, __pyx_L1_error)

    /* "View.MemoryView":1314
 * 
 *         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 == 0)) __PYX_ERR(6, 1314, __pyx_L1_error)

    /* "View.MemoryView":1310
 *             return 0
 * 
 *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
 * 
 * 
 */
  }

  /* "View.MemoryView":1316
 *         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":1317
 * 
 *     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":1318
 *     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":1320
 *     refcount_copying(&dst, dtype_is_object, ndim, True)
 * 
 *     free(tmpdata)             # <<<<<<<<<<<<<<
 *     return 0
 * 
 */
  free(__pyx_v_tmpdata);

  /* "View.MemoryView":1321
 * 
 *     free(tmpdata)
 *     return 0             # <<<<<<<<<<<<<<
 * 
 * @cname('__pyx_memoryview_broadcast_leading')
 */
  __pyx_r = 0;
  goto __pyx_L0;

  /* "View.MemoryView":1252
 * 
 * @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:;
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    #ifdef WITH_THREAD
    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
    #endif
    __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
    #ifdef WITH_THREAD
    PyGILState_Release(__pyx_gilstate_save);
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/* "View.MemoryView":1324
 * 
 * @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;

  /* "View.MemoryView":1328
 *                             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":1330
 *     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 > -1L; __pyx_t_1-=1) {
    __pyx_v_i = __pyx_t_1;

    /* "View.MemoryView":1331
 * 
 *     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":1332
 *     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":1333
 *         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":1335
 *         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;
  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
    __pyx_v_i = __pyx_t_2;

    /* "View.MemoryView":1336
 * 
 *     for i in range(offset):
 *         mslice.shape[i] = 1             # <<<<<<<<<<<<<<
 *         mslice.strides[i] = mslice.strides[0]
 *         mslice.suboffsets[i] = -1
 */
    (__pyx_v_mslice->shape[__pyx_v_i]) = 1;

    /* "View.MemoryView":1337
 *     for i in range(offset):
 *         mslice.shape[i] = 1
 *         mslice.strides[i] = mslice.strides[0]             # <<<<<<<<<<<<<<
 *         mslice.suboffsets[i] = -1
 * 
 */
    (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]);

    /* "View.MemoryView":1338
 *         mslice.shape[i] = 1
 *         mslice.strides[i] = mslice.strides[0]
 *         mslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
 * 
 * 
 */
    (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L;
  }

  /* "View.MemoryView":1324
 * 
 * @cname('__pyx_memoryview_broadcast_leading')
 * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
 *                             int ndim,
 *                             int ndim_other) nogil:
 */

  /* function exit code */
}

/* "View.MemoryView":1346
 * 
 * @cname('__pyx_memoryview_refcount_copying')
 * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
 *                            int ndim, bint inc) nogil:
 * 
 */

static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) {
  int __pyx_t_1;

  /* "View.MemoryView":1350
 * 
 * 
 *     if dtype_is_object:             # <<<<<<<<<<<<<<
 *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,
 *                                            dst.strides, ndim, inc)
 */
  __pyx_t_1 = (__pyx_v_dtype_is_object != 0);
  if (__pyx_t_1) {

    /* "View.MemoryView":1351
 * 
 *     if dtype_is_object:
 *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,             # <<<<<<<<<<<<<<
 *                                            dst.strides, ndim, inc)
 * 
 */
    __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc);

    /* "View.MemoryView":1350
 * 
 * 
 *     if dtype_is_object:             # <<<<<<<<<<<<<<
 *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,
 *                                            dst.strides, ndim, inc)
 */
  }

  /* "View.MemoryView":1346
 * 
 * @cname('__pyx_memoryview_refcount_copying')
 * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
 *                            int ndim, bint inc) nogil:
 * 
 */

  /* function exit code */
}

/* "View.MemoryView":1355
 * 
 * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
 * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
 *                                              Py_ssize_t *strides, int ndim,
 *                                              bint inc) with gil:
 */

static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {
  __Pyx_RefNannyDeclarations
  #ifdef WITH_THREAD
  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
  #endif
  __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0);

  /* "View.MemoryView":1358
 *                                              Py_ssize_t *strides, int ndim,
 *                                              bint inc) with gil:
 *     refcount_objects_in_slice(data, shape, strides, ndim, inc)             # <<<<<<<<<<<<<<
 * 
 * @cname('__pyx_memoryview_refcount_objects_in_slice')
 */
  __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc);

  /* "View.MemoryView":1355
 * 
 * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
 * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
 *                                              Py_ssize_t *strides, int ndim,
 *                                              bint inc) with gil:
 */

  /* function exit code */
  __Pyx_RefNannyFinishContext();
  #ifdef WITH_THREAD
  PyGILState_Release(__pyx_gilstate_save);
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/* "View.MemoryView":1361
 * 
 * @cname('__pyx_memoryview_refcount_objects_in_slice')
 * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
 *                                     Py_ssize_t *strides, int ndim, bint inc):
 *     cdef Py_ssize_t i
 */

static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {
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    /* "View.MemoryView":1366
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        /* "View.MemoryView":1368
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      /* "View.MemoryView":1373
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 *         data += strides[0]
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 *         data += strides[0]             # <<<<<<<<<<<<<<
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  /* "View.MemoryView":1361
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 * @cname('__pyx_memoryview_refcount_objects_in_slice')
 * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
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/* "View.MemoryView":1381
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static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) {

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 *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,
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 * 
 * 
 */
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  /* "View.MemoryView":1381
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 * @cname('__pyx_memoryview_slice_assign_scalar')
 * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim,             # <<<<<<<<<<<<<<
 *                               size_t itemsize, void *item,
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 */

  /* function exit code */
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/* "View.MemoryView":1391
 * 
 * @cname('__pyx_memoryview__slice_assign_scalar')
 * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
 *                               Py_ssize_t *strides, int ndim,
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 */

static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) {
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  Py_ssize_t __pyx_t_2;
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  /* "View.MemoryView":1395
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 *     cdef Py_ssize_t extent = shape[0]
 * 
 */
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  /* "View.MemoryView":1396
 *     cdef Py_ssize_t i
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 *     cdef Py_ssize_t extent = shape[0]             # <<<<<<<<<<<<<<
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 *     if ndim == 1:
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  __pyx_v_extent = (__pyx_v_shape[0]);

  /* "View.MemoryView":1398
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 * 
 *     if ndim == 1:             # <<<<<<<<<<<<<<
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  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
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    /* "View.MemoryView":1399
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 *     if ndim == 1:
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    __pyx_t_2 = __pyx_v_extent;
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      /* "View.MemoryView":1400
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 *             data += stride
 *     else:
 */
      memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize);

      /* "View.MemoryView":1401
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 *             data += stride             # <<<<<<<<<<<<<<
 *     else:
 *         for i in range(extent):
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      __pyx_v_data = (__pyx_v_data + __pyx_v_stride);
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    /* "View.MemoryView":1398
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 *     if ndim == 1:             # <<<<<<<<<<<<<<
 *         for i in range(extent):
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  /* "View.MemoryView":1403
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      /* "View.MemoryView":1404
 *     else:
 *         for i in range(extent):
 *             _slice_assign_scalar(data, shape + 1, strides + 1,             # <<<<<<<<<<<<<<
 *                                 ndim - 1, itemsize, item)
 *             data += stride
 */
      __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item);

      /* "View.MemoryView":1406
 *             _slice_assign_scalar(data, shape + 1, strides + 1,
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 *             data += stride             # <<<<<<<<<<<<<<
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 * 
 */
      __pyx_v_data = (__pyx_v_data + __pyx_v_stride);
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  /* "View.MemoryView":1391
 * 
 * @cname('__pyx_memoryview__slice_assign_scalar')
 * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
 *                               Py_ssize_t *strides, int ndim,
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  /* function exit code */
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static PyObject *__pyx_tp_new_11randomstate_13mlfg_1279_861_RandomState(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
  struct __pyx_obj_11randomstate_13mlfg_1279_861_RandomState *p;
  PyObject *o;
  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
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static void __pyx_tp_dealloc_11randomstate_13mlfg_1279_861_RandomState(PyObject *o) {
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static PyMethodDef __pyx_methods_11randomstate_13mlfg_1279_861_RandomState[] = {
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  {"set_state", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_11set_state, METH_O, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_10set_state},
  {"random_raw", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_13random_raw, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_12random_raw},
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  {"__reduce__", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_19__reduce__, METH_NOARGS, 0},
  {"random_sample", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_21random_sample, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_20random_sample},
  {"tomaxint", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_23tomaxint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_22tomaxint},
  {"randint", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_25randint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_24randint},
  {"bytes", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_27bytes, METH_O, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_26bytes},
  {"choice", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_29choice, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_28choice},
  {"uniform", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_31uniform, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_30uniform},
  {"rand", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_33rand, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_32rand},
  {"randn", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_35randn, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_34randn},
  {"random_integers", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_37random_integers, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_36random_integers},
  {"standard_normal", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_39standard_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_38standard_normal},
  {"normal", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_41normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_40normal},
  {"beta", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_43beta, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_42beta},
  {"exponential", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_45exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_44exponential},
  {"standard_exponential", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_47standard_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_46standard_exponential},
  {"standard_gamma", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_49standard_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_48standard_gamma},
  {"gamma", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_51gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_50gamma},
  {"f", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_53f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_52f},
  {"noncentral_f", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_55noncentral_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_54noncentral_f},
  {"chisquare", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_57chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_56chisquare},
  {"noncentral_chisquare", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_59noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_58noncentral_chisquare},
  {"standard_cauchy", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_61standard_cauchy, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_60standard_cauchy},
  {"standard_t", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_63standard_t, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_62standard_t},
  {"vonmises", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_65vonmises, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_64vonmises},
  {"pareto", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_67pareto, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_66pareto},
  {"weibull", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_69weibull, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_68weibull},
  {"power", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_71power, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_70power},
  {"laplace", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_73laplace, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_72laplace},
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  {"triangular", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_85triangular, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_84triangular},
  {"binomial", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_87binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_86binomial},
  {"negative_binomial", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_89negative_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_88negative_binomial},
  {"poisson", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_91poisson, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_90poisson},
  {"zipf", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_93zipf, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_92zipf},
  {"geometric", (PyCFunction)__pyx_pw_11randomstate_13mlfg_1279_861_11RandomState_95geometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_11randomstate_13mlfg_1279_861_11RandomState_94geometric},
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  "\nRandomState(seed=None)\n\nContainer for a Multiplicative Lagged Fibonacci Generator (MLFG).\n\nLFG(1279, 861, \\*) is a 64-bit implementation of an MLFG that uses lags 1279\nand 861 where random numbers are determined by\n\n.. math::\n\n   x_n = (x_{n-k} * x_{n-l}) \\mathrm{Mod} 2^M\n\nwhere k=861, l=1279 and M=64. The period of the generator is\n2**1340 - 2**61.  Even though the PRNG uses 64 bits, only 63 are random\nsince all numbers in x must be odd.\n\n``mlfg_1279_861.RandomState`` exposes a number of methods for generating random\nnumbers drawn from a variety of probability distributions. In addition to the\ndistribution-specific arguments, each method takes a keyword argument\n``size`` that defaults to ``None``. If ``size`` is ``None``, then a single\nvalue is generated and returned. If `size` is an integer, then a 1-D\narray filled with generated values is returned. If ``size`` is a tuple,\nthen an array with that shape is filled and returned.\n\n**No Compatibility Guarantee**\n\n``mlfg_1279_861.RandomState`` does not make a guarantee that a fixed seed and a\nfixed series of calls to ``mlfg_1279_861.RandomState`` methods using the same\nparameters will always produce the same results. This is different from\n``numpy.random.RandomState`` guarantee. This is done to simplify improving\nrandom number generators.  To ensure identical results, you must use the\nsame release version.\n\nParameters\n----------\nseed : {None, int, array_like}, optional\n    Random seed initializing the pseudo-random number generator.\n    Can be an integer in [0, 2**64-1], array of integers in\n    [0, 2**64-1] or ``None`` (the default). If `seed` is ``None``,\n    then ``mlfg_1279_861.RandomState`` will try to read entropy from\n    ``/dev/urandom`` (or the Windows analog) if available to\n    produce a 64-bit seed. If unavailable, a 64-bit hash of the time\n    and process ID is used.\n\nNotes\n-----\nThe state of the MLFG(1279,861,*) PRNG is represented by 1279 64-bit unsigned""\nintegers as well as a two 32-bit integers representing the location in the\nstate array of the current and lagged values.\n\n**State and Seeding**\n\nThe ``mlfg_1279_861.RandomState`` state vector consists of a 1279 element array\nof 64-bit unsigned integers plus a two integers value between 0 and 1278\nindicating  the current position and the position of the lagged value within\nthe main array required to produce the next random. All 1279 elements of the\nstate array must be odd.\n\n``mlfg_1279_861.RandomState`` is seeded using either a single 64-bit unsigned integer\nor a vector of 64-bit unsigned integers.  In either case, the input seed is\nused as an input (or inputs) for another simple random number generator,\nSplitmix64, and the output of this PRNG function is used as the initial state.\nUsing a single 64-bit value for the seed can only initialize a small range of\nthe possible initial state values.  When using an array, the SplitMix64 state\nfor producing the ith component of the initial state is XORd with the ith\nvalue of the seed array until the seed array is exhausted. When using an array\nthe initial state for the SplitMix64 state is 0 so that using a single element\narray and using the same value as a scalar will produce the same initial state.\n", /*tp_doc*/
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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) {
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}

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},
  {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)
  "randomstate.mlfg_1279_861.memoryview", /*tp_name*/
  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
  0, /*tp_print*/
  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
};
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 PY_VERSION_HEX >= 0x030400a1
  if (unlikely(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);
    ++Py_REFCNT(o);
    __pyx_memoryviewslice___dealloc__(o);
    --Py_REFCNT(o);
    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[] = {
  {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)
  "randomstate.mlfg_1279_861._memoryviewslice", /*tp_name*/
  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
  0, /*tp_print*/
  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
};

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

#if PY_MAJOR_VERSION >= 3
static struct PyModuleDef __pyx_moduledef = {
  #if PY_VERSION_HEX < 0x03020000
    { PyObject_HEAD_INIT(NULL) NULL, 0, NULL },
  #else
    PyModuleDef_HEAD_INIT,
  #endif
    "mlfg_1279_861",
    0, /* m_doc */
    -1, /* m_size */
    __pyx_methods /* m_methods */,
    NULL, /* m_reload */
    NULL, /* m_traverse */
    NULL, /* m_clear */
    NULL /* m_free */
};
#endif

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  {&__pyx_n_s_less, __pyx_k_less, sizeof(__pyx_k_less), 0, 0, 1, 1},
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  {&__pyx_n_s_loc, __pyx_k_loc, sizeof(__pyx_k_loc), 0, 0, 1, 1},
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  {&__pyx_n_s_logistic, __pyx_k_logistic, sizeof(__pyx_k_logistic), 0, 0, 1, 1},
  {&__pyx_kp_u_logistic_loc_0_0_scale_1_0_size, __pyx_k_logistic_loc_0_0_scale_1_0_size, sizeof(__pyx_k_logistic_loc_0_0_scale_1_0_size), 0, 1, 0, 0},
  {&__pyx_n_s_lognormal, __pyx_k_lognormal, sizeof(__pyx_k_lognormal), 0, 0, 1, 1},
  {&__pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si, __pyx_k_lognormal_mean_0_0_sigma_1_0_si, sizeof(__pyx_k_lognormal_mean_0_0_sigma_1_0_si), 0, 1, 0, 0},
  {&__pyx_n_s_logseries, __pyx_k_logseries, sizeof(__pyx_k_logseries), 0, 0, 1, 1},
  {&__pyx_kp_u_logseries_p_size_None_Draw_samp, __pyx_k_logseries_p_size_None_Draw_samp, sizeof(__pyx_k_logseries_p_size_None_Draw_samp), 0, 1, 0, 0},
  {&__pyx_n_s_long, __pyx_k_long, sizeof(__pyx_k_long), 0, 0, 1, 1},
  {&__pyx_n_s_low, __pyx_k_low, sizeof(__pyx_k_low), 0, 0, 1, 1},
  {&__pyx_kp_s_low_high, __pyx_k_low_high, sizeof(__pyx_k_low_high), 0, 0, 1, 0},
  {&__pyx_kp_s_low_is_out_of_bounds_for_s, __pyx_k_low_is_out_of_bounds_for_s, sizeof(__pyx_k_low_is_out_of_bounds_for_s), 0, 0, 1, 0},
  {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
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  {&__pyx_n_s_maxsize, __pyx_k_maxsize, sizeof(__pyx_k_maxsize), 0, 0, 1, 1},
  {&__pyx_n_s_mean, __pyx_k_mean, sizeof(__pyx_k_mean), 0, 0, 1, 1},
  {&__pyx_kp_s_mean_and_cov_must_have_same_leng, __pyx_k_mean_and_cov_must_have_same_leng, sizeof(__pyx_k_mean_and_cov_must_have_same_leng), 0, 0, 1, 0},
  {&__pyx_kp_s_mean_must_be_1_dimensional, __pyx_k_mean_must_be_1_dimensional, sizeof(__pyx_k_mean_must_be_1_dimensional), 0, 0, 1, 0},
  {&__pyx_n_s_memview, __pyx_k_memview, sizeof(__pyx_k_memview), 0, 0, 1, 1},
  {&__pyx_n_s_method, __pyx_k_method, sizeof(__pyx_k_method), 0, 0, 1, 1},
  {&__pyx_kp_u_mlfg_1279_861, __pyx_k_mlfg_1279_861, sizeof(__pyx_k_mlfg_1279_861), 0, 1, 0, 0},
  {&__pyx_n_u_mlfg_1279_861_2, __pyx_k_mlfg_1279_861_2, sizeof(__pyx_k_mlfg_1279_861_2), 0, 1, 0, 1},
  {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1},
  {&__pyx_kp_s_mode_right, __pyx_k_mode_right, sizeof(__pyx_k_mode_right), 0, 0, 1, 0},
  {&__pyx_n_s_mu, __pyx_k_mu, sizeof(__pyx_k_mu), 0, 0, 1, 1},
  {&__pyx_n_s_multinomial, __pyx_k_multinomial, sizeof(__pyx_k_multinomial), 0, 0, 1, 1},
  {&__pyx_kp_u_multinomial_n_pvals_size_None_D, __pyx_k_multinomial_n_pvals_size_None_D, sizeof(__pyx_k_multinomial_n_pvals_size_None_D), 0, 1, 0, 0},
  {&__pyx_n_s_multivariate_normal, __pyx_k_multivariate_normal, sizeof(__pyx_k_multivariate_normal), 0, 0, 1, 1},
  {&__pyx_kp_u_multivariate_normal_mean_cov_si, __pyx_k_multivariate_normal_mean_cov_si, sizeof(__pyx_k_multivariate_normal_mean_cov_si), 0, 1, 0, 0},
  {&__pyx_n_s_n, __pyx_k_n, sizeof(__pyx_k_n), 0, 0, 1, 1},
  {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1},
  {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1},
  {&__pyx_n_s_nbad, __pyx_k_nbad, sizeof(__pyx_k_nbad), 0, 0, 1, 1},
  {&__pyx_kp_s_nbad_0, __pyx_k_nbad_0, sizeof(__pyx_k_nbad_0), 0, 0, 1, 0},
  {&__pyx_kp_u_ndarray_is_not_C_contiguous, __pyx_k_ndarray_is_not_C_contiguous, sizeof(__pyx_k_ndarray_is_not_C_contiguous), 0, 1, 0, 0},
  {&__pyx_kp_u_ndarray_is_not_Fortran_contiguou, __pyx_k_ndarray_is_not_Fortran_contiguou, sizeof(__pyx_k_ndarray_is_not_Fortran_contiguou), 0, 1, 0, 0},
  {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1},
  {&__pyx_n_s_negative_binomial, __pyx_k_negative_binomial, sizeof(__pyx_k_negative_binomial), 0, 0, 1, 1},
  {&__pyx_kp_u_negative_binomial_n_p_size_None, __pyx_k_negative_binomial_n_p_size_None, sizeof(__pyx_k_negative_binomial_n_p_size_None), 0, 1, 0, 0},
  {&__pyx_n_s_ngood, __pyx_k_ngood, sizeof(__pyx_k_ngood), 0, 0, 1, 1},
  {&__pyx_kp_s_ngood_0, __pyx_k_ngood_0, sizeof(__pyx_k_ngood_0), 0, 0, 1, 0},
  {&__pyx_kp_s_ngood_nbad_nsample, __pyx_k_ngood_nbad_nsample, sizeof(__pyx_k_ngood_nbad_nsample), 0, 0, 1, 0},
  {&__pyx_n_s_nonc, __pyx_k_nonc, sizeof(__pyx_k_nonc), 0, 0, 1, 1},
  {&__pyx_n_s_noncentral_chisquare, __pyx_k_noncentral_chisquare, sizeof(__pyx_k_noncentral_chisquare), 0, 0, 1, 1},
  {&__pyx_kp_u_noncentral_chisquare_df_nonc_si, __pyx_k_noncentral_chisquare_df_nonc_si, sizeof(__pyx_k_noncentral_chisquare_df_nonc_si), 0, 1, 0, 0},
  {&__pyx_n_s_noncentral_f, __pyx_k_noncentral_f, sizeof(__pyx_k_noncentral_f), 0, 0, 1, 1},
  {&__pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s, __pyx_k_noncentral_f_dfnum_dfden_nonc_s, sizeof(__pyx_k_noncentral_f_dfnum_dfden_nonc_s), 0, 1, 0, 0},
  {&__pyx_n_s_normal, __pyx_k_normal, sizeof(__pyx_k_normal), 0, 0, 1, 1},
  {&__pyx_kp_u_normal_loc_0_0_scale_1_0_size_N, __pyx_k_normal_loc_0_0_scale_1_0_size_N, sizeof(__pyx_k_normal_loc_0_0_scale_1_0_size_N), 0, 1, 0, 0},
  {&__pyx_n_s_normal_method, __pyx_k_normal_method, sizeof(__pyx_k_normal_method), 0, 0, 1, 1},
  {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1},
  {&__pyx_n_s_nsample, __pyx_k_nsample, sizeof(__pyx_k_nsample), 0, 0, 1, 1},
  {&__pyx_kp_s_nsample_1, __pyx_k_nsample_1, sizeof(__pyx_k_nsample_1), 0, 0, 1, 0},
  {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1},
  {&__pyx_n_s_numpy_dual, __pyx_k_numpy_dual, sizeof(__pyx_k_numpy_dual), 0, 0, 1, 1},
  {&__pyx_n_s_obj, __pyx_k_obj, sizeof(__pyx_k_obj), 0, 0, 1, 1},
  {&__pyx_n_s_object, __pyx_k_object, sizeof(__pyx_k_object), 0, 0, 1, 1},
  {&__pyx_n_s_operator, __pyx_k_operator, sizeof(__pyx_k_operator), 0, 0, 1, 1},
  {&__pyx_n_s_p, __pyx_k_p, sizeof(__pyx_k_p), 0, 0, 1, 1},
  {&__pyx_kp_s_p_must_be_1_dimensional, __pyx_k_p_must_be_1_dimensional, sizeof(__pyx_k_p_must_be_1_dimensional), 0, 0, 1, 0},
  {&__pyx_n_s_pack, __pyx_k_pack, sizeof(__pyx_k_pack), 0, 0, 1, 1},
  {&__pyx_n_s_pareto, __pyx_k_pareto, sizeof(__pyx_k_pareto), 0, 0, 1, 1},
  {&__pyx_kp_u_pareto_a_size_None_Draw_samples, __pyx_k_pareto_a_size_None_Draw_samples, sizeof(__pyx_k_pareto_a_size_None_Draw_samples), 0, 1, 0, 0},
  {&__pyx_n_s_permutation, __pyx_k_permutation, sizeof(__pyx_k_permutation), 0, 0, 1, 1},
  {&__pyx_kp_u_permutation_x_Randomly_permute, __pyx_k_permutation_x_Randomly_permute, sizeof(__pyx_k_permutation_x_Randomly_permute), 0, 1, 0, 0},
  {&__pyx_n_s_poisson, __pyx_k_poisson, sizeof(__pyx_k_poisson), 0, 0, 1, 1},
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  {&__pyx_n_s_prng, __pyx_k_prng, sizeof(__pyx_k_prng), 0, 0, 1, 1},
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  {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1},
  {&__pyx_n_s_rand, __pyx_k_rand, sizeof(__pyx_k_rand), 0, 0, 1, 1},
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  {&__pyx_n_s_random_integers, __pyx_k_random_integers, sizeof(__pyx_k_random_integers), 0, 0, 1, 1},
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  {&__pyx_n_s_return_index, __pyx_k_return_index, sizeof(__pyx_k_return_index), 0, 0, 1, 1},
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  {&__pyx_n_s_seed, __pyx_k_seed, sizeof(__pyx_k_seed), 0, 0, 1, 1},
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  {&__pyx_n_s_set_state, __pyx_k_set_state, sizeof(__pyx_k_set_state), 0, 0, 1, 1},
  {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1},
  {&__pyx_n_s_shuffle, __pyx_k_shuffle, sizeof(__pyx_k_shuffle), 0, 0, 1, 1},
  {&__pyx_kp_u_shuffle_x_Modify_a_sequence_in, __pyx_k_shuffle_x_Modify_a_sequence_in, sizeof(__pyx_k_shuffle_x_Modify_a_sequence_in), 0, 1, 0, 0},
  {&__pyx_n_s_side, __pyx_k_side, sizeof(__pyx_k_side), 0, 0, 1, 1},
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  {&__pyx_n_s_signbit, __pyx_k_signbit, sizeof(__pyx_k_signbit), 0, 0, 1, 1},
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  {&__pyx_n_s_sqrt, __pyx_k_sqrt, sizeof(__pyx_k_sqrt), 0, 0, 1, 1},
  {&__pyx_n_s_squeeze, __pyx_k_squeeze, sizeof(__pyx_k_squeeze), 0, 0, 1, 1},
  {&__pyx_n_s_standard_cauchy, __pyx_k_standard_cauchy, sizeof(__pyx_k_standard_cauchy), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_cauchy_size_None_Draw, __pyx_k_standard_cauchy_size_None_Draw, sizeof(__pyx_k_standard_cauchy_size_None_Draw), 0, 1, 0, 0},
  {&__pyx_n_s_standard_exponential, __pyx_k_standard_exponential, sizeof(__pyx_k_standard_exponential), 0, 0, 1, 1},
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  {&__pyx_n_s_standard_gamma, __pyx_k_standard_gamma, sizeof(__pyx_k_standard_gamma), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_gamma_shape_size_None, __pyx_k_standard_gamma_shape_size_None, sizeof(__pyx_k_standard_gamma_shape_size_None), 0, 1, 0, 0},
  {&__pyx_n_s_standard_normal, __pyx_k_standard_normal, sizeof(__pyx_k_standard_normal), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_normal_size_None_dtype, __pyx_k_standard_normal_size_None_dtype, sizeof(__pyx_k_standard_normal_size_None_dtype), 0, 1, 0, 0},
  {&__pyx_n_s_standard_t, __pyx_k_standard_t, sizeof(__pyx_k_standard_t), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_t_df_size_None_Draw_sa, __pyx_k_standard_t_df_size_None_Draw_sa, sizeof(__pyx_k_standard_t_df_size_None_Draw_sa), 0, 1, 0, 0},
  {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1},
  {&__pyx_n_s_state, __pyx_k_state, sizeof(__pyx_k_state), 0, 0, 1, 1},
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  {&__pyx_n_s_stream, __pyx_k_stream, sizeof(__pyx_k_stream), 0, 0, 1, 1},
  {&__pyx_kp_s_strided_and_direct, __pyx_k_strided_and_direct, sizeof(__pyx_k_strided_and_direct), 0, 0, 1, 0},
  {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0},
  {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0},
  {&__pyx_n_s_strides, __pyx_k_strides, sizeof(__pyx_k_strides), 0, 0, 1, 1},
  {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1},
  {&__pyx_n_s_subtract, __pyx_k_subtract, sizeof(__pyx_k_subtract), 0, 0, 1, 1},
  {&__pyx_n_s_sum, __pyx_k_sum, sizeof(__pyx_k_sum), 0, 0, 1, 1},
  {&__pyx_kp_s_sum_pvals_1_1_0, __pyx_k_sum_pvals_1_1_0, sizeof(__pyx_k_sum_pvals_1_1_0), 0, 0, 1, 0},
  {&__pyx_n_s_svd, __pyx_k_svd, sizeof(__pyx_k_svd), 0, 0, 1, 1},
  {&__pyx_n_s_sys, __pyx_k_sys, sizeof(__pyx_k_sys), 0, 0, 1, 1},
  {&__pyx_n_s_take, __pyx_k_take, sizeof(__pyx_k_take), 0, 0, 1, 1},
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 *         with lock:             # <<<<<<<<<<<<<<
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 *         with lock:             # <<<<<<<<<<<<<<
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 *         with lock:             # <<<<<<<<<<<<<<
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 *         cnt = np.PyArray_SIZE(array)
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 *     off = <bool_t>(<bool_t>low)
 *     if size is None:
 *         with lock:             # <<<<<<<<<<<<<<
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 *         cnt = np.PyArray_SIZE(array)
 *         out = <bool_t *>np.PyArray_DATA(array)
 *         with lock, nogil:             # <<<<<<<<<<<<<<
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  /* "randomstate/mlfg_1279_861.pyx":147
 *         return seeds[0]
 * 
 *     seed = long(0)             # <<<<<<<<<<<<<<
 *     for i in range(nbytes):
 *         scale = 2 ** (32 * i)
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 *                     idx = operator.index(seed)
 *                     if idx < 0:
 *                         raise ValueError('seed < 0')             # <<<<<<<<<<<<<<
 *                 else:
 *                     self.__seed = seed = _generate_seed(RS_SEED_NBYTES)
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 *                     seed = np.asarray(seed).astype(np.object, casting='safe')
 *                     if ((seed > int(2**64 - 1)) | (seed < 0)).any():
 *                         raise ValueError("Seed values must be between 0 and "             # <<<<<<<<<<<<<<
 *                                          "18446744073709551616 (2**64-1)")
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 *                                          "18446744073709551616 (2**64-1)")
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 *         _set_state(&self.rng_state, state['state'])
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 * 
 *         if size is None:
 *             with self.lock:             # <<<<<<<<<<<<<<
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 */
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 * 
 *         with self.lock, nogil:             # <<<<<<<<<<<<<<
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 * 
 *         if size is None:
 *             with self.lock:             # <<<<<<<<<<<<<<
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 */
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 * 
 *         for i in range(n):
 *             with self.lock, nogil:             # <<<<<<<<<<<<<<
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 *             raise ValueError("high is out of bounds for %s" % (key,))
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 *             raise ValueError("low >= high")             # <<<<<<<<<<<<<<
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 *         if key == 'int32':
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 *         """
 *         cdef Py_ssize_t n_uint32 = ((length - 1) // 4 + 1)
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 * 
 */
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 *                 pop_size = operator.index(a.item())
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 *                 raise ValueError("a must be greater than 0")
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 *             if pop_size <= 0:
 *                 raise ValueError("a must be greater than 0")             # <<<<<<<<<<<<<<
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 *                 raise ValueError("a must be greater than 0")
 *         elif a.ndim != 1:
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 *             if pop_size is 0:
 *                 raise ValueError("a must be non-empty")             # <<<<<<<<<<<<<<
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 *         if p is not None:
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 * 
 *             if p.ndim != 1:
 *                 raise ValueError("p must be 1-dimensional")             # <<<<<<<<<<<<<<
 *             if p.size != pop_size:
 *                 raise ValueError("a and p must have same size")
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 *                 raise ValueError("p must be 1-dimensional")
 *             if p.size != pop_size:
 *                 raise ValueError("a and p must have same size")             # <<<<<<<<<<<<<<
 *             if np.logical_or.reduce(p < 0):
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 *                 raise ValueError("a and p must have same size")
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 *                 raise ValueError("probabilities are not non-negative")             # <<<<<<<<<<<<<<
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 *                 raise ValueError("probabilities do not sum to 1")
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 *                 raise ValueError("probabilities are not non-negative")
 *             if abs(kahan_sum(pix, d) - 1.) > atol:
 *                 raise ValueError("probabilities do not sum to 1")             # <<<<<<<<<<<<<<
 * 
 *         shape = size
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 *         else:
 *             if size > pop_size:
 *                 raise ValueError("Cannot take a larger sample than "             # <<<<<<<<<<<<<<
 *                                  "population when 'replace=False'")
 * 
 */
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 *             if p is not None:
 *                 if np.count_nonzero(p > 0) < size:
 *                     raise ValueError("Fewer non-zero entries in p than size")             # <<<<<<<<<<<<<<
 *                 n_uniq = 0
 *                 p = p.copy()
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  __pyx_tuple__70 = PyTuple_Pack(1, __pyx_kp_s_Fewer_non_zero_entries_in_p_than); if (unlikely(!__pyx_tuple__70)) __PYX_ERR(2, 1046, __pyx_L1_error)
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 *         if shape is None and isinstance(idx, np.ndarray):
 *             # In most cases a scalar will have been made an array
 *             idx = idx.item(0)             # <<<<<<<<<<<<<<
 * 
 *         #Use samples as indices for a if a is array-like
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 *             # array, taking into account that np.array(item) may not work
 *             # for object arrays.
 *             res = np.empty((), dtype=a.dtype)             # <<<<<<<<<<<<<<
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 *             range = _high - _low
 *             if not np.isfinite(range):
 *                 raise OverflowError('Range exceeds valid bounds')             # <<<<<<<<<<<<<<
 * 
 *             return cont(&self.rng_state, &random_uniform, size, self.lock, 2,
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  __pyx_tuple__73 = PyTuple_Pack(1, __pyx_kp_s_Range_exceeds_valid_bounds); if (unlikely(!__pyx_tuple__73)) __PYX_ERR(2, 1179, __pyx_L1_error)
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  /* "randomstate/mlfg_1279_861.pyx":1191
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    if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
#else
    if (likely(PyCFunction_Check(func))) {
#endif
        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
            return __Pyx_PyObject_CallMethO(func, arg);
        }
    }
    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

/* BufferFormatCheck */
    static CYTHON_INLINE int __Pyx_IsLittleEndian(void) {
  unsigned int n = 1;
  return *(unsigned char*)(&n) != 0;
}
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 '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 '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 '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 CYTHON_INLINE PyObject *
__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
{
    const char *ts = *tsp;
    int i = 0, number;
    int ndim = ctx->head->field->type->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;
    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_IsLittleEndian()) {
          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
          return NULL;
        }
        ctx->new_packmode = '=';
        ++ts;
        break;
      case '>':
      case '!':
        if (__Pyx_IsLittleEndian()) {
          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;
        }
      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->enc_count += ctx->new_count;
          ctx->new_count = 1;
          got_Z = 0;
          ++ts;
          break;
        }
      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;
        }
    }
  }
}
static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) {
  buf->buf = NULL;
  buf->obj = NULL;
  buf->strides = __Pyx_zeros;
  buf->shape = __Pyx_zeros;
  buf->suboffsets = __Pyx_minusones;
}
static CYTHON_INLINE int __Pyx_GetBufferAndValidate(
        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
{
  if (obj == Py_None || obj == NULL) {
    __Pyx_ZeroBuffer(buf);
    return 0;
  }
  buf->buf = NULL;
  if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail;
  if (buf->ndim != nd) {
    PyErr_Format(PyExc_ValueError,
                 "Buffer has wrong number of dimensions (expected %d, got %d)",
                 nd, buf->ndim);
    goto fail;
  }
  if (!cast) {
    __Pyx_BufFmt_Context ctx;
    __Pyx_BufFmt_Init(&ctx, stack, dtype);
    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
  }
  if ((unsigned)buf->itemsize != dtype->size) {
    PyErr_Format(PyExc_ValueError,
      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
    goto fail;
  }
  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
  return 0;
fail:;
  __Pyx_ZeroBuffer(buf);
  return -1;
}
static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
  if (info->buf == NULL) return;
  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
  __Pyx_ReleaseBuffer(info);
}

/* 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 (!buf) {
        PyErr_SetString(PyExc_ValueError,
            "buf is NULL.");
        goto fail;
    } else if (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;
}
static CYTHON_INLINE void __pyx_fatalerror(const char *fmt, ...) {
    va_list vargs;
    char msg[200];
#ifdef HAVE_STDARG_PROTOTYPES
    va_start(vargs, fmt);
#else
    va_start(vargs);
#endif
    vsnprintf(msg, 200, fmt, vargs);
    Py_FatalError(msg);
    va_end(vargs);
}
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 (!memview || (PyObject *) memview == Py_None)
        return;
    if (__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 (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 (!memview ) {
        return;
    } else if ((PyObject *) memview == Py_None) {
        memslice->memview = NULL;
        return;
    }
    if (__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 (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;
    }
}

/* GetItemInt */
      static CYTHON_INLINE 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_COMPILING_IN_CPYTHON
    if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
    if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
        PyObject *r = PyList_GET_ITEM(o, 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_COMPILING_IN_CPYTHON
    if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
    if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
        PyObject *r = PyTuple_GET_ITEM(o, 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_COMPILING_IN_CPYTHON
    if (is_list || PyList_CheckExact(o)) {
        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
        if ((!boundscheck) || (likely((n >= 0) & (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((n >= 0) & (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));
}

/* PyErrFetchRestore */
      #if CYTHON_COMPILING_IN_CPYTHON
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;
    tmp_tb = tstate->curexc_traceback;
    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
}

/* 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 PY_VERSION_HEX >= 0x03030000
    if (cause) {
#else
    if (cause && cause != Py_None) {
#endif
        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 = PyThreadState_GET();
        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

/* 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(PyObject_TypeCheck(obj, type)))
        return 1;
    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
                 Py_TYPE(obj)->tp_name, type->tp_name);
    return 0;
}

/* PyObjectCallNoArg */
        #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
#ifdef __Pyx_CyFunction_USED
    if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
#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

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

/* SaveResetException */
          #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    *type = tstate->exc_type;
    *value = tstate->exc_value;
    *tb = tstate->exc_traceback;
    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;
    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;
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
}
#endif

/* GetException */
          #if CYTHON_COMPILING_IN_CPYTHON
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_COMPILING_IN_CPYTHON
    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_COMPILING_IN_CPYTHON
    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_COMPILING_IN_CPYTHON
    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;
    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;
}

/* PyIntBinop */
            #if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int 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 && PY_MAJOR_VERSION >= 3
    if (likely(PyLong_CheckExact(op1))) {
        const long b = intval;
        long a;
        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;
                    }
                case 2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    }
                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;
                    }
                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;
                    }
                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;
                    }
                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;
                    }
                #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15
                default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ);
                #else
                default: Py_RETURN_FALSE;
                #endif
            }
        }
            if (a == b) {
                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);
}
#endif

/* PyNumberPow2 */
            static PyObject* __Pyx__PyNumber_PowerOf2(PyObject *two, PyObject *exp, PyObject *none, int inplace) {
#if CYTHON_COMPILING_IN_CPYTHON
    Py_ssize_t shiftby;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_CheckExact(exp))) {
        shiftby = PyInt_AS_LONG(exp);
    } else
#endif
    if (likely(PyLong_CheckExact(exp))) {
        #if CYTHON_USE_PYLONG_INTERNALS
        const Py_ssize_t size = Py_SIZE(exp);
        if (likely(size == 1)) {
            shiftby = ((PyLongObject*)exp)->ob_digit[0];
        } else if (size == 0) {
            return PyInt_FromLong(1L);
        } else if (unlikely(size < 0)) {
            goto fallback;
        } else {
            shiftby = PyLong_AsSsize_t(exp);
        }
        #else
        shiftby = PyLong_AsSsize_t(exp);
        #endif
    } else {
        goto fallback;
    }
    if (likely(shiftby >= 0)) {
        if ((size_t)shiftby <= sizeof(long) * 8 - 2) {
            long value = 1L << shiftby;
            return PyInt_FromLong(value);
        } else if ((size_t)shiftby <= sizeof(unsigned PY_LONG_LONG) * 8 - 1) {
            unsigned PY_LONG_LONG value = ((unsigned PY_LONG_LONG)1) << shiftby;
            return PyLong_FromUnsignedLongLong(value);
        } else {
            PyObject *one = PyInt_FromLong(1L);
            if (unlikely(!one)) return NULL;
            return PyNumber_Lshift(one, exp);
        }
    } else if (shiftby == -1 && PyErr_Occurred()) {
        PyErr_Clear();
    }
fallback:
#endif
    return (inplace ? PyNumber_InPlacePower : PyNumber_Power)(two, exp, none);
}

/* StringJoin */
            #if !CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyBytes_Join(PyObject* sep, PyObject* values) {
    return PyObject_CallMethodObjArgs(sep, __pyx_n_s_join, values, NULL);
}
#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_CheckExact(key)) || 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
                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(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
                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(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);
}

/* PyErrExceptionMatches */
            #if CYTHON_COMPILING_IN_CPYTHON
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;
    return PyErr_GivenExceptionMatches(exc_type, err);
}
#endif

/* 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 = 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;
        }
        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_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
}

/* 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_COMPILING_IN_CPYTHON
    PyThreadState *tstate = PyThreadState_GET();
    PyObject* exc_type = tstate->curexc_type;
    if (unlikely(exc_type)) {
        if (likely(exc_type == PyExc_StopIteration) || 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;
}

/* PyIntBinop */
            #if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
    #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_subtract(op1, op2);
    }
    #endif
    #if CYTHON_USE_PYLONG_INTERNALS && PY_MAJOR_VERSION >= 3
    if (likely(PyLong_CheckExact(op1))) {
        const long b = intval;
        long a, x;
        const PY_LONG_LONG llb = intval;
        PY_LONG_LONG lla, llx;
        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;
                    } 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;
                    }
                case 2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    } 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;
                    }
                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;
                    } 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;
                    }
                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;
                    } 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;
                    }
                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;
                    } 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;
                    }
                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;
                    } 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;
                    }
                default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
            }
        }
                x = a - b;
            return PyLong_FromLong(x);
        long_long:
                llx = lla - llb;
            return PyLong_FromLongLong(llx);
    }
    #endif
    if (PyFloat_CheckExact(op1)) {
        const long b = intval;
        double a = PyFloat_AS_DOUBLE(op1);
            double result;
            PyFPE_START_PROTECT("subtract", return NULL)
            result = ((double)a) - (double)b;
            PyFPE_END_PROTECT(result)
            return PyFloat_FromDouble(result);
    }
    return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2);
}
#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_COMPILING_IN_CPYTHON
    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_COMPILING_IN_CPYTHON
        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;
}

/* 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_COMPILING_IN_CPYTHON
    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_COMPILING_IN_CPYTHON
        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;
}

/* PyIntBinop */
            #if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
    #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 && PY_MAJOR_VERSION >= 3
    if (likely(PyLong_CheckExact(op1))) {
        const long b = intval;
        long a, x;
        const PY_LONG_LONG llb = intval;
        PY_LONG_LONG lla, llx;
        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;
                    } 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;
                    }
                case 2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    } 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;
                    }
                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;
                    } 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;
                    }
                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;
                    } 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;
                    }
                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;
                    } 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;
                    }
                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;
                    } 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;
                    }
                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
            }
        }
                x = a + b;
            return PyLong_FromLong(x);
        long_long:
                llx = lla + llb;
            return PyLong_FromLongLong(llx);
    }
    #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

/* 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_VERSION_HEX < 0x03030000
    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 (strchr(__Pyx_MODULE_NAME, '.')) {
                #if PY_VERSION_HEX < 0x03030000
                PyObject *py_level = PyInt_FromLong(1);
                if (!py_level)
                    goto bad;
                module = PyObject_CallFunctionObjArgs(py_import,
                    name, global_dict, empty_dict, list, py_level, NULL);
                Py_DECREF(py_level);
                #else
                module = PyImport_ImportModuleLevelObject(
                    name, global_dict, empty_dict, list, 1);
                #endif
                if (!module) {
                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
                        goto bad;
                    PyErr_Clear();
                }
            }
            level = 0;
        }
        #endif
        if (!module) {
            #if PY_VERSION_HEX < 0x03030000
            PyObject *py_level = PyInt_FromLong(level);
            if (!py_level)
                goto bad;
            module = PyObject_CallFunctionObjArgs(py_import,
                name, global_dict, empty_dict, list, py_level, NULL);
            Py_DECREF(py_level);
            #else
            module = PyImport_ImportModuleLevelObject(
                name, global_dict, empty_dict, list, level);
            #endif
        }
    }
bad:
    #if PY_VERSION_HEX < 0x03030000
    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;
}

/* SetItemInt */
            static CYTHON_INLINE 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_COMPILING_IN_CPYTHON
    if (is_list || PyList_CheckExact(o)) {
        Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o));
        if ((!boundscheck) || likely((n >= 0) & (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);
}

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

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

/* GetAttr */
              static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
#if CYTHON_COMPILING_IN_CPYTHON
#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);
}

/* 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;
    }
    length = stop - start;
    if (unlikely(length <= 0))
        return PyUnicode_FromUnicode(NULL, 0);
    cstring += start;
    if (decode_func) {
        return decode_func(cstring, length, errors);
    } else {
        return PyUnicode_Decode(cstring, length, encoding, errors);
    }
}

/* SwapException */
              #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    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;
    *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

/* 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;
}

/* 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;
    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(
        PyThreadState_GET(), /*PyThreadState *tstate,*/
        py_code,             /*PyCodeObject *code,*/
        __pyx_d,      /*PyObject *globals,*/
        0                    /*PyObject *locals*/
    );
    if (!py_frame) goto bad;
    py_frame->f_lineno = 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 (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
        if (PyObject_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
        if (PyObject_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 (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; }
    Py_DECREF(obj);
    view->obj = NULL;
}
#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_uint64_t(uint64_t value) {
    const uint64_t neg_one = (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);
        } else if (sizeof(uint64_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(uint64_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(uint64_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        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);
    }
}

/* MemviewDtypeToObject */
              static CYTHON_INLINE PyObject *__pyx_memview_get_nn_uint64_t(const char *itemp) {
    return (PyObject *) __Pyx_PyInt_From_uint64_t(*(uint64_t *) itemp);
}
static CYTHON_INLINE int __pyx_memview_set_nn_uint64_t(const char *itemp, PyObject *obj) {
    uint64_t value = __Pyx_PyInt_As_uint64_t(obj);
    if ((value == (uint64_t)-1) && PyErr_Occurred())
        return 0;
    *(uint64_t *) itemp = value;
    return 1;
}

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

/* CIntToPy */
              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
    const long neg_one = (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);
        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(long) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        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_Py_intptr_t(Py_intptr_t value) {
    const Py_intptr_t neg_one = (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);
        } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(Py_intptr_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        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);
    }
}

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

/* MemviewDtypeToObject */
              static CYTHON_INLINE PyObject *__pyx_memview_get_nn___pyx_t_5numpy_int_t(const char *itemp) {
    return (PyObject *) __Pyx_PyInt_From_npy_long(*(__pyx_t_5numpy_int_t *) itemp);
}
static CYTHON_INLINE int __pyx_memview_set_nn___pyx_t_5numpy_int_t(const char *itemp, PyObject *obj) {
    __pyx_t_5numpy_int_t value = __Pyx_PyInt_As_npy_long(obj);
    if ((value == (npy_long)-1) && PyErr_Occurred())
        return 0;
    *(__pyx_t_5numpy_int_t *) itemp = value;
    return 1;
}

/* CIntToPy */
              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int64_t(int64_t value) {
    const int64_t neg_one = (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);
        } else if (sizeof(int64_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(int64_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int64_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        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_int32_t(int32_t value) {
    const int32_t neg_one = (int32_t) -1, const_zero = (int32_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(int32_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int32_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(int32_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(int32_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int32_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(int32_t),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int16_t(int16_t value) {
    const int16_t neg_one = (int16_t) -1, const_zero = (int16_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(int16_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int16_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(int16_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(int16_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int16_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(int16_t),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int8_t(int8_t value) {
    const int8_t neg_one = (int8_t) -1, const_zero = (int8_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(int8_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int8_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(int8_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(int8_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int8_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(int8_t),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint32_t(uint32_t value) {
    const uint32_t neg_one = (uint32_t) -1, const_zero = (uint32_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(uint32_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(uint32_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(uint32_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(uint32_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(uint32_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(uint32_t),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint16_t(uint16_t value) {
    const uint16_t neg_one = (uint16_t) -1, const_zero = (uint16_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(uint16_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(uint16_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(uint16_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(uint16_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(uint16_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(uint16_t),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint8_t(uint8_t value) {
    const uint8_t neg_one = (uint8_t) -1, const_zero = (uint8_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(uint8_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(uint8_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(uint8_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
        }
    } else {
        if (sizeof(uint8_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(uint8_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(uint8_t),
                                     little, !is_unsigned);
    }
}

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

/* None */
              #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

/* None */
              #if CYTHON_CCOMPLEX
#else
    static CYTHON_INLINE int __Pyx_c_eqf(__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_sumf(__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_difff(__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_prodf(__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;
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        __pyx_t_float_complex z;
        float denom = b.real * b.real + b.imag * b.imag;
        z.real = (a.real * b.real + a.imag * b.imag) / denom;
        z.imag = (a.imag * b.real - a.real * b.imag) / denom;
        return z;
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__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_zerof(__pyx_t_float_complex a) {
       return (a.real == 0) && (a.imag == 0);
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__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_absf(__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_powf(__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:
                        z = __Pyx_c_prodf(a, a);
                        return __Pyx_c_prodf(a, a);
                    case 3:
                        z = __Pyx_c_prodf(a, a);
                        return __Pyx_c_prodf(z, a);
                    case 4:
                        z = __Pyx_c_prodf(a, a);
                        return __Pyx_c_prodf(z, z);
                }
            }
            if (a.imag == 0) {
                if (a.real == 0) {
                    return a;
                }
                r = a.real;
                theta = 0;
            } else {
                r = __Pyx_c_absf(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

/* None */
              #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

/* None */
              #if CYTHON_CCOMPLEX
#else
    static CYTHON_INLINE int __Pyx_c_eq(__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(__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(__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(__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;
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        __pyx_t_double_complex z;
        double denom = b.real * b.real + b.imag * b.imag;
        z.real = (a.real * b.real + a.imag * b.imag) / denom;
        z.imag = (a.imag * b.real - a.real * b.imag) / denom;
        return z;
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__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(__pyx_t_double_complex a) {
       return (a.real == 0) && (a.imag == 0);
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__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(__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(__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:
                        z = __Pyx_c_prod(a, a);
                        return __Pyx_c_prod(a, a);
                    case 3:
                        z = __Pyx_c_prod(a, a);
                        return __Pyx_c_prod(z, a);
                    case 4:
                        z = __Pyx_c_prod(a, a);
                        return __Pyx_c_prod(z, z);
                }
            }
            if (a.imag == 0) {
                if (a.real == 0) {
                    return a;
                }
                r = a.real;
                theta = 0;
            } else {
                r = __Pyx_c_abs(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

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

/* 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 (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 uint64_t __Pyx_PyInt_As_uint64_t(PyObject *x) {
    const uint64_t neg_one = (uint64_t) -1, const_zero = (uint64_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(uint64_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(uint64_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (uint64_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 (uint64_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(uint64_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(uint64_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint64_t) >= 2 * PyLong_SHIFT) {
                            return (uint64_t) (((((uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(uint64_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint64_t) >= 3 * PyLong_SHIFT) {
                            return (uint64_t) (((((((uint64_t)digits[2]) << PyLong_SHIFT) | (uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(uint64_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_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(uint64_t) >= 4 * PyLong_SHIFT) {
                            return (uint64_t) (((((((((uint64_t)digits[3]) << PyLong_SHIFT) | (uint64_t)digits[2]) << PyLong_SHIFT) | (uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_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 (uint64_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(uint64_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint64_t, unsigned long, PyLong_AsUnsignedLong(x))
            } else if (sizeof(uint64_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint64_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (uint64_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(uint64_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(uint64_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(uint64_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint64_t) - 1 > 2 * PyLong_SHIFT) {
                            return (uint64_t) (((uint64_t)-1)*(((((uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(uint64_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint64_t) - 1 > 2 * PyLong_SHIFT) {
                            return (uint64_t) ((((((uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(uint64_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint64_t) - 1 > 3 * PyLong_SHIFT) {
                            return (uint64_t) (((uint64_t)-1)*(((((((uint64_t)digits[2]) << PyLong_SHIFT) | (uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(uint64_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint64_t) - 1 > 3 * PyLong_SHIFT) {
                            return (uint64_t) ((((((((uint64_t)digits[2]) << PyLong_SHIFT) | (uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(uint64_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_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(uint64_t) - 1 > 4 * PyLong_SHIFT) {
                            return (uint64_t) (((uint64_t)-1)*(((((((((uint64_t)digits[3]) << PyLong_SHIFT) | (uint64_t)digits[2]) << PyLong_SHIFT) | (uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(uint64_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint64_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(uint64_t) - 1 > 4 * PyLong_SHIFT) {
                            return (uint64_t) ((((((((((uint64_t)digits[3]) << PyLong_SHIFT) | (uint64_t)digits[2]) << PyLong_SHIFT) | (uint64_t)digits[1]) << PyLong_SHIFT) | (uint64_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(uint64_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint64_t, long, PyLong_AsLong(x))
            } else if (sizeof(uint64_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint64_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            uint64_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 (uint64_t) -1;
        }
    } else {
        uint64_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (uint64_t) -1;
        val = __Pyx_PyInt_As_uint64_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to uint64_t");
    return (uint64_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to uint64_t");
    return (uint64_t) -1;
}

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

/* CIntFromPy */
              static CYTHON_INLINE int64_t __Pyx_PyInt_As_int64_t(PyObject *x) {
    const int64_t neg_one = (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))
            } else if (sizeof(int64_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int64_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } 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))
            } else if (sizeof(int64_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int64_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#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 uint32_t __Pyx_PyInt_As_uint32_t(PyObject *x) {
    const uint32_t neg_one = (uint32_t) -1, const_zero = (uint32_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(uint32_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(uint32_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (uint32_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 (uint32_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(uint32_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(uint32_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint32_t) >= 2 * PyLong_SHIFT) {
                            return (uint32_t) (((((uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(uint32_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint32_t) >= 3 * PyLong_SHIFT) {
                            return (uint32_t) (((((((uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(uint32_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_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(uint32_t) >= 4 * PyLong_SHIFT) {
                            return (uint32_t) (((((((((uint32_t)digits[3]) << PyLong_SHIFT) | (uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_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 (uint32_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(uint32_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint32_t, unsigned long, PyLong_AsUnsignedLong(x))
            } else if (sizeof(uint32_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint32_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (uint32_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(uint32_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(uint32_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(uint32_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint32_t) - 1 > 2 * PyLong_SHIFT) {
                            return (uint32_t) (((uint32_t)-1)*(((((uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(uint32_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint32_t) - 1 > 2 * PyLong_SHIFT) {
                            return (uint32_t) ((((((uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(uint32_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint32_t) - 1 > 3 * PyLong_SHIFT) {
                            return (uint32_t) (((uint32_t)-1)*(((((((uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(uint32_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint32_t) - 1 > 3 * PyLong_SHIFT) {
                            return (uint32_t) ((((((((uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(uint32_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_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(uint32_t) - 1 > 4 * PyLong_SHIFT) {
                            return (uint32_t) (((uint32_t)-1)*(((((((((uint32_t)digits[3]) << PyLong_SHIFT) | (uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(uint32_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint32_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(uint32_t) - 1 > 4 * PyLong_SHIFT) {
                            return (uint32_t) ((((((((((uint32_t)digits[3]) << PyLong_SHIFT) | (uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(uint32_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint32_t, long, PyLong_AsLong(x))
            } else if (sizeof(uint32_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint32_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            uint32_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 (uint32_t) -1;
        }
    } else {
        uint32_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (uint32_t) -1;
        val = __Pyx_PyInt_As_uint32_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to uint32_t");
    return (uint32_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to uint32_t");
    return (uint32_t) -1;
}

/* CIntFromPy */
              static CYTHON_INLINE int32_t __Pyx_PyInt_As_int32_t(PyObject *x) {
    const int32_t neg_one = (int32_t) -1, const_zero = (int32_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(int32_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(int32_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (int32_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 (int32_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(int32_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(int32_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int32_t) >= 2 * PyLong_SHIFT) {
                            return (int32_t) (((((int32_t)digits[1]) << PyLong_SHIFT) | (int32_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int32_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int32_t) >= 3 * PyLong_SHIFT) {
                            return (int32_t) (((((((int32_t)digits[2]) << PyLong_SHIFT) | (int32_t)digits[1]) << PyLong_SHIFT) | (int32_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int32_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_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(int32_t) >= 4 * PyLong_SHIFT) {
                            return (int32_t) (((((((((int32_t)digits[3]) << PyLong_SHIFT) | (int32_t)digits[2]) << PyLong_SHIFT) | (int32_t)digits[1]) << PyLong_SHIFT) | (int32_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 (int32_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(int32_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int32_t, unsigned long, PyLong_AsUnsignedLong(x))
            } else if (sizeof(int32_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int32_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int32_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(int32_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(int32_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(int32_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int32_t) - 1 > 2 * PyLong_SHIFT) {
                            return (int32_t) (((int32_t)-1)*(((((int32_t)digits[1]) << PyLong_SHIFT) | (int32_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(int32_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int32_t) - 1 > 2 * PyLong_SHIFT) {
                            return (int32_t) ((((((int32_t)digits[1]) << PyLong_SHIFT) | (int32_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(int32_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int32_t) - 1 > 3 * PyLong_SHIFT) {
                            return (int32_t) (((int32_t)-1)*(((((((int32_t)digits[2]) << PyLong_SHIFT) | (int32_t)digits[1]) << PyLong_SHIFT) | (int32_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int32_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int32_t) - 1 > 3 * PyLong_SHIFT) {
                            return (int32_t) ((((((((int32_t)digits[2]) << PyLong_SHIFT) | (int32_t)digits[1]) << PyLong_SHIFT) | (int32_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(int32_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_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(int32_t) - 1 > 4 * PyLong_SHIFT) {
                            return (int32_t) (((int32_t)-1)*(((((((((int32_t)digits[3]) << PyLong_SHIFT) | (int32_t)digits[2]) << PyLong_SHIFT) | (int32_t)digits[1]) << PyLong_SHIFT) | (int32_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int32_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int32_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(int32_t) - 1 > 4 * PyLong_SHIFT) {
                            return (int32_t) ((((((((((int32_t)digits[3]) << PyLong_SHIFT) | (int32_t)digits[2]) << PyLong_SHIFT) | (int32_t)digits[1]) << PyLong_SHIFT) | (int32_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(int32_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int32_t, long, PyLong_AsLong(x))
            } else if (sizeof(int32_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int32_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            int32_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 (int32_t) -1;
        }
    } else {
        int32_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (int32_t) -1;
        val = __Pyx_PyInt_As_int32_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to int32_t");
    return (int32_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to int32_t");
    return (int32_t) -1;
}

/* CIntFromPy */
              static CYTHON_INLINE uint16_t __Pyx_PyInt_As_uint16_t(PyObject *x) {
    const uint16_t neg_one = (uint16_t) -1, const_zero = (uint16_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(uint16_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(uint16_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (uint16_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 (uint16_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(uint16_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(uint16_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint16_t) >= 2 * PyLong_SHIFT) {
                            return (uint16_t) (((((uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(uint16_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint16_t) >= 3 * PyLong_SHIFT) {
                            return (uint16_t) (((((((uint16_t)digits[2]) << PyLong_SHIFT) | (uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(uint16_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_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(uint16_t) >= 4 * PyLong_SHIFT) {
                            return (uint16_t) (((((((((uint16_t)digits[3]) << PyLong_SHIFT) | (uint16_t)digits[2]) << PyLong_SHIFT) | (uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_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 (uint16_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(uint16_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint16_t, unsigned long, PyLong_AsUnsignedLong(x))
            } else if (sizeof(uint16_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint16_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (uint16_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(uint16_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(uint16_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(uint16_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint16_t) - 1 > 2 * PyLong_SHIFT) {
                            return (uint16_t) (((uint16_t)-1)*(((((uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(uint16_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint16_t) - 1 > 2 * PyLong_SHIFT) {
                            return (uint16_t) ((((((uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(uint16_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint16_t) - 1 > 3 * PyLong_SHIFT) {
                            return (uint16_t) (((uint16_t)-1)*(((((((uint16_t)digits[2]) << PyLong_SHIFT) | (uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(uint16_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint16_t) - 1 > 3 * PyLong_SHIFT) {
                            return (uint16_t) ((((((((uint16_t)digits[2]) << PyLong_SHIFT) | (uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(uint16_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_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(uint16_t) - 1 > 4 * PyLong_SHIFT) {
                            return (uint16_t) (((uint16_t)-1)*(((((((((uint16_t)digits[3]) << PyLong_SHIFT) | (uint16_t)digits[2]) << PyLong_SHIFT) | (uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(uint16_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint16_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(uint16_t) - 1 > 4 * PyLong_SHIFT) {
                            return (uint16_t) ((((((((((uint16_t)digits[3]) << PyLong_SHIFT) | (uint16_t)digits[2]) << PyLong_SHIFT) | (uint16_t)digits[1]) << PyLong_SHIFT) | (uint16_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(uint16_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint16_t, long, PyLong_AsLong(x))
            } else if (sizeof(uint16_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint16_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            uint16_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 (uint16_t) -1;
        }
    } else {
        uint16_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (uint16_t) -1;
        val = __Pyx_PyInt_As_uint16_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to uint16_t");
    return (uint16_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to uint16_t");
    return (uint16_t) -1;
}

/* CIntFromPy */
              static CYTHON_INLINE int16_t __Pyx_PyInt_As_int16_t(PyObject *x) {
    const int16_t neg_one = (int16_t) -1, const_zero = (int16_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(int16_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(int16_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (int16_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 (int16_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(int16_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(int16_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int16_t) >= 2 * PyLong_SHIFT) {
                            return (int16_t) (((((int16_t)digits[1]) << PyLong_SHIFT) | (int16_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int16_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int16_t) >= 3 * PyLong_SHIFT) {
                            return (int16_t) (((((((int16_t)digits[2]) << PyLong_SHIFT) | (int16_t)digits[1]) << PyLong_SHIFT) | (int16_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int16_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_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(int16_t) >= 4 * PyLong_SHIFT) {
                            return (int16_t) (((((((((int16_t)digits[3]) << PyLong_SHIFT) | (int16_t)digits[2]) << PyLong_SHIFT) | (int16_t)digits[1]) << PyLong_SHIFT) | (int16_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 (int16_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(int16_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int16_t, unsigned long, PyLong_AsUnsignedLong(x))
            } else if (sizeof(int16_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int16_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int16_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(int16_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(int16_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(int16_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int16_t) - 1 > 2 * PyLong_SHIFT) {
                            return (int16_t) (((int16_t)-1)*(((((int16_t)digits[1]) << PyLong_SHIFT) | (int16_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(int16_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int16_t) - 1 > 2 * PyLong_SHIFT) {
                            return (int16_t) ((((((int16_t)digits[1]) << PyLong_SHIFT) | (int16_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(int16_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int16_t) - 1 > 3 * PyLong_SHIFT) {
                            return (int16_t) (((int16_t)-1)*(((((((int16_t)digits[2]) << PyLong_SHIFT) | (int16_t)digits[1]) << PyLong_SHIFT) | (int16_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int16_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int16_t) - 1 > 3 * PyLong_SHIFT) {
                            return (int16_t) ((((((((int16_t)digits[2]) << PyLong_SHIFT) | (int16_t)digits[1]) << PyLong_SHIFT) | (int16_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(int16_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_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(int16_t) - 1 > 4 * PyLong_SHIFT) {
                            return (int16_t) (((int16_t)-1)*(((((((((int16_t)digits[3]) << PyLong_SHIFT) | (int16_t)digits[2]) << PyLong_SHIFT) | (int16_t)digits[1]) << PyLong_SHIFT) | (int16_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int16_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int16_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(int16_t) - 1 > 4 * PyLong_SHIFT) {
                            return (int16_t) ((((((((((int16_t)digits[3]) << PyLong_SHIFT) | (int16_t)digits[2]) << PyLong_SHIFT) | (int16_t)digits[1]) << PyLong_SHIFT) | (int16_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(int16_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int16_t, long, PyLong_AsLong(x))
            } else if (sizeof(int16_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int16_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            int16_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 (int16_t) -1;
        }
    } else {
        int16_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (int16_t) -1;
        val = __Pyx_PyInt_As_int16_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to int16_t");
    return (int16_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to int16_t");
    return (int16_t) -1;
}

/* CIntFromPy */
              static CYTHON_INLINE uint8_t __Pyx_PyInt_As_uint8_t(PyObject *x) {
    const uint8_t neg_one = (uint8_t) -1, const_zero = (uint8_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(uint8_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(uint8_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (uint8_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 (uint8_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(uint8_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(uint8_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint8_t) >= 2 * PyLong_SHIFT) {
                            return (uint8_t) (((((uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(uint8_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint8_t) >= 3 * PyLong_SHIFT) {
                            return (uint8_t) (((((((uint8_t)digits[2]) << PyLong_SHIFT) | (uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(uint8_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_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(uint8_t) >= 4 * PyLong_SHIFT) {
                            return (uint8_t) (((((((((uint8_t)digits[3]) << PyLong_SHIFT) | (uint8_t)digits[2]) << PyLong_SHIFT) | (uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_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 (uint8_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(uint8_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint8_t, unsigned long, PyLong_AsUnsignedLong(x))
            } else if (sizeof(uint8_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint8_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (uint8_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(uint8_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(uint8_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(uint8_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint8_t) - 1 > 2 * PyLong_SHIFT) {
                            return (uint8_t) (((uint8_t)-1)*(((((uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(uint8_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint8_t) - 1 > 2 * PyLong_SHIFT) {
                            return (uint8_t) ((((((uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(uint8_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint8_t) - 1 > 3 * PyLong_SHIFT) {
                            return (uint8_t) (((uint8_t)-1)*(((((((uint8_t)digits[2]) << PyLong_SHIFT) | (uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(uint8_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(uint8_t) - 1 > 3 * PyLong_SHIFT) {
                            return (uint8_t) ((((((((uint8_t)digits[2]) << PyLong_SHIFT) | (uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(uint8_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_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(uint8_t) - 1 > 4 * PyLong_SHIFT) {
                            return (uint8_t) (((uint8_t)-1)*(((((((((uint8_t)digits[3]) << PyLong_SHIFT) | (uint8_t)digits[2]) << PyLong_SHIFT) | (uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(uint8_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(uint8_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(uint8_t) - 1 > 4 * PyLong_SHIFT) {
                            return (uint8_t) ((((((((((uint8_t)digits[3]) << PyLong_SHIFT) | (uint8_t)digits[2]) << PyLong_SHIFT) | (uint8_t)digits[1]) << PyLong_SHIFT) | (uint8_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(uint8_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint8_t, long, PyLong_AsLong(x))
            } else if (sizeof(uint8_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(uint8_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            uint8_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 (uint8_t) -1;
        }
    } else {
        uint8_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (uint8_t) -1;
        val = __Pyx_PyInt_As_uint8_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to uint8_t");
    return (uint8_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to uint8_t");
    return (uint8_t) -1;
}

/* CIntFromPy */
              static CYTHON_INLINE int8_t __Pyx_PyInt_As_int8_t(PyObject *x) {
    const int8_t neg_one = (int8_t) -1, const_zero = (int8_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(int8_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(int8_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (int8_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 (int8_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(int8_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(int8_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int8_t) >= 2 * PyLong_SHIFT) {
                            return (int8_t) (((((int8_t)digits[1]) << PyLong_SHIFT) | (int8_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int8_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int8_t) >= 3 * PyLong_SHIFT) {
                            return (int8_t) (((((((int8_t)digits[2]) << PyLong_SHIFT) | (int8_t)digits[1]) << PyLong_SHIFT) | (int8_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int8_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_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(int8_t) >= 4 * PyLong_SHIFT) {
                            return (int8_t) (((((((((int8_t)digits[3]) << PyLong_SHIFT) | (int8_t)digits[2]) << PyLong_SHIFT) | (int8_t)digits[1]) << PyLong_SHIFT) | (int8_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 (int8_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(int8_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int8_t, unsigned long, PyLong_AsUnsignedLong(x))
            } else if (sizeof(int8_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int8_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int8_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(int8_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(int8_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(int8_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int8_t) - 1 > 2 * PyLong_SHIFT) {
                            return (int8_t) (((int8_t)-1)*(((((int8_t)digits[1]) << PyLong_SHIFT) | (int8_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(int8_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int8_t) - 1 > 2 * PyLong_SHIFT) {
                            return (int8_t) ((((((int8_t)digits[1]) << PyLong_SHIFT) | (int8_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(int8_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int8_t) - 1 > 3 * PyLong_SHIFT) {
                            return (int8_t) (((int8_t)-1)*(((((((int8_t)digits[2]) << PyLong_SHIFT) | (int8_t)digits[1]) << PyLong_SHIFT) | (int8_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int8_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int8_t) - 1 > 3 * PyLong_SHIFT) {
                            return (int8_t) ((((((((int8_t)digits[2]) << PyLong_SHIFT) | (int8_t)digits[1]) << PyLong_SHIFT) | (int8_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(int8_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_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(int8_t) - 1 > 4 * PyLong_SHIFT) {
                            return (int8_t) (((int8_t)-1)*(((((((((int8_t)digits[3]) << PyLong_SHIFT) | (int8_t)digits[2]) << PyLong_SHIFT) | (int8_t)digits[1]) << PyLong_SHIFT) | (int8_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int8_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int8_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(int8_t) - 1 > 4 * PyLong_SHIFT) {
                            return (int8_t) ((((((((((int8_t)digits[3]) << PyLong_SHIFT) | (int8_t)digits[2]) << PyLong_SHIFT) | (int8_t)digits[1]) << PyLong_SHIFT) | (int8_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(int8_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int8_t, long, PyLong_AsLong(x))
            } else if (sizeof(int8_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int8_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            int8_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 (int8_t) -1;
        }
    } else {
        int8_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (int8_t) -1;
        val = __Pyx_PyInt_As_int8_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to int8_t");
    return (int8_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to int8_t");
    return (int8_t) -1;
}

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

/* CIntFromPy */
              static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) {
    const size_t neg_one = (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))
            } else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } 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))
            } else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#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 char __Pyx_PyInt_As_char(PyObject *x) {
    const char neg_one = (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))
            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } 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))
            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#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;
}

/* CIntFromPy */
              static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
    const long neg_one = (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))
            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
            }
        } 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))
            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
            }
        }
        {
#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;
}

/* 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 (buf->strides[dim] != sizeof(void *)) {
                    PyErr_Format(PyExc_ValueError,
                                 "Buffer is not indirectly contiguous "
                                 "in dimension %d.", dim);
                    goto fail;
                }
            } else if (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 (stride < buf->itemsize) {
                PyErr_SetString(PyExc_ValueError,
                                "Buffer and memoryview are not contiguous "
                                "in the same dimension.");
                goto fail;
            }
        }
    } else {
        if (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 (spec & (__Pyx_MEMVIEW_PTR)) {
            PyErr_Format(PyExc_ValueError,
                         "C-contiguous buffer is not indirect in "
                         "dimension %d", dim);
            goto fail;
        } else if (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 (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 (!buf->suboffsets || (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 (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 (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 (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 (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
    }
    if ((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;
    }
    for (i = 0; i < ndim; i++) {
        spec = axes_specs[i];
        if (!__pyx_check_strides(buf, i, ndim, spec))
            goto fail;
        if (!__pyx_check_suboffsets(buf, i, ndim, spec))
            goto fail;
    }
    if (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_ds_nn_uint64_t(PyObject *obj) {
    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
    __Pyx_BufFmt_StackElem stack[1];
    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
    int retcode;
    if (obj == Py_None) {
        result.memview = (struct __pyx_memoryview_obj *) Py_None;
        return result;
    }
    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
                                                 PyBUF_RECORDS, 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;
}

/* ObjectToMemviewSlice */
              static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_nn___pyx_t_5numpy_int_t(PyObject *obj) {
    __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 | PyBUF_WRITABLE), 1,
                                                 &__Pyx_TypeInfo_nn___pyx_t_5numpy_int_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;
}

/* ModuleImport */
              #ifndef __PYX_HAVE_RT_ImportModule
#define __PYX_HAVE_RT_ImportModule
static PyObject *__Pyx_ImportModule(const char *name) {
    PyObject *py_name = 0;
    PyObject *py_module = 0;
    py_name = __Pyx_PyIdentifier_FromString(name);
    if (!py_name)
        goto bad;
    py_module = PyImport_Import(py_name);
    Py_DECREF(py_name);
    return py_module;
bad:
    Py_XDECREF(py_name);
    return 0;
}
#endif

/* TypeImport */
              #ifndef __PYX_HAVE_RT_ImportType
#define __PYX_HAVE_RT_ImportType
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
    size_t size, int strict)
{
    PyObject *py_module = 0;
    PyObject *result = 0;
    PyObject *py_name = 0;
    char warning[200];
    Py_ssize_t basicsize;
#ifdef Py_LIMITED_API
    PyObject *py_basicsize;
#endif
    py_module = __Pyx_ImportModule(module_name);
    if (!py_module)
        goto bad;
    py_name = __Pyx_PyIdentifier_FromString(class_name);
    if (!py_name)
        goto bad;
    result = PyObject_GetAttr(py_module, py_name);
    Py_DECREF(py_name);
    py_name = 0;
    Py_DECREF(py_module);
    py_module = 0;
    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 (!strict && (size_t)basicsize > size) {
        PyOS_snprintf(warning, sizeof(warning),
            "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd",
            module_name, class_name, basicsize, size);
        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
    }
    else if ((size_t)basicsize != size) {
        PyErr_Format(PyExc_ValueError,
            "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd",
            module_name, class_name, basicsize, size);
        goto bad;
    }
    return (PyTypeObject *)result;
bad:
    Py_XDECREF(py_module);
    Py_XDECREF(result);
    return NULL;
}
#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;
        ++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 char* __Pyx_PyObject_AsString(PyObject* o) {
    Py_ssize_t ignore;
    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
}
static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
#if CYTHON_COMPILING_IN_CPYTHON && (__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)) {
#if PY_VERSION_HEX < 0x03030000
        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
        if (__Pyx_PyUnicode_READY(o) == -1) return NULL;
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
        if (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
    } 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 PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
  PyNumberMethods *m;
  const char *name = NULL;
  PyObject *res = NULL;
#if PY_MAJOR_VERSION < 3
  if (PyInt_Check(x) || PyLong_Check(x))
#else
  if (PyLong_Check(x))
#endif
    return __Pyx_NewRef(x);
  m = Py_TYPE(x)->tp_as_number;
#if PY_MAJOR_VERSION < 3
  if (m && m->nb_int) {
    name = "int";
    res = PyNumber_Int(x);
  }
  else if (m && m->nb_long) {
    name = "long";
    res = PyNumber_Long(x);
  }
#else
  if (m && m->nb_int) {
    name = "int";
    res = PyNumber_Long(x);
  }
#endif
  if (res) {
#if PY_MAJOR_VERSION < 3
    if (!PyInt_Check(res) && !PyLong_Check(res)) {
#else
    if (!PyLong_Check(res)) {
#endif
      PyErr_Format(PyExc_TypeError,
                   "__%.4s__ returned non-%.4s (type %.200s)",
                   name, name, Py_TYPE(res)->tp_name);
      Py_DECREF(res);
      return NULL;
    }
  }
  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(x);
  }
#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_PyInt_FromSize_t(size_t ival) {
    return PyInt_FromSize_t(ival);
}


#endif /* Py_PYTHON_H */
