Merge pull request #14898 from eric-wieser/delete-generated-file

MAINT: Delete and ignore generated files

3 files changed, 3 insertions, 1593 deletions

diff --git a/numpy/random/.gitignore b/numpy/random/.gitignore
new file mode 100644
index 000000000..fea3f955a
--- /dev/null
+++ b/numpy/random/.gitignore
@@ -0,0 +1,3 @@
+# generated files
+_bounded_integers.pyx
+_bounded_integers.pxd
diff --git a/numpy/random/_bounded_integers.pxd b/numpy/random/_bounded_integers.pxd
deleted file mode 100644
index d3ee97a70..000000000
--- a/numpy/random/_bounded_integers.pxd
+++ /dev/null
@@ -1,29 +0,0 @@
-from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
-                          int8_t, int16_t, int32_t, int64_t, intptr_t)
-import numpy as np
-cimport numpy as np
-ctypedef np.npy_bool bool_t
-
-from ._bit_generator cimport bitgen_t
-
-cdef inline uint64_t _gen_mask(uint64_t max_val) nogil:
-    """Mask generator for use in bounded random numbers"""
-    # Smallest bit mask >= max
-    cdef uint64_t mask = max_val
-    mask |= mask >> 1
-    mask |= mask >> 2
-    mask |= mask >> 4
-    mask |= mask >> 8
-    mask |= mask >> 16
-    mask |= mask >> 32
-    return mask
-
-cdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
-cdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
-cdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
-cdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
-cdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
-cdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
-cdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
-cdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
-cdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
diff --git a/numpy/random/_bounded_integers.pyx b/numpy/random/_bounded_integers.pyx
deleted file mode 100644
index d6a534b43..000000000
--- a/numpy/random/_bounded_integers.pyx
+++ /dev/null
@@ -1,1564 +0,0 @@
-#!python
-#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True
-
-import numpy as np
-cimport numpy as np
-
-__all__ = []
-
-np.import_array()
-
-cdef extern from "include/distributions.h":
-    # Generate random numbers in closed interval [off, off + rng].
-    uint64_t random_bounded_uint64(bitgen_t *bitgen_state,
-                                   uint64_t off, uint64_t rng,
-                                   uint64_t mask, bint use_masked) nogil
-    uint32_t random_buffered_bounded_uint32(bitgen_t *bitgen_state,
-                                            uint32_t off, uint32_t rng,
-                                            uint32_t mask, bint use_masked,
-                                            int *bcnt, uint32_t *buf) nogil
-    uint16_t random_buffered_bounded_uint16(bitgen_t *bitgen_state,
-                                            uint16_t off, uint16_t rng,
-                                            uint16_t mask, bint use_masked,
-                                            int *bcnt, uint32_t *buf) nogil
-    uint8_t random_buffered_bounded_uint8(bitgen_t *bitgen_state,
-                                          uint8_t off, uint8_t rng,
-                                          uint8_t mask, bint use_masked,
-                                          int *bcnt, uint32_t *buf) nogil
-    np.npy_bool random_buffered_bounded_bool(bitgen_t *bitgen_state,
-                                             np.npy_bool off, np.npy_bool rng,
-                                             np.npy_bool mask, bint use_masked,
-                                             int *bcnt, uint32_t *buf) nogil
-    void random_bounded_uint64_fill(bitgen_t *bitgen_state,
-                                    uint64_t off, uint64_t rng, np.npy_intp cnt,
-                                    bint use_masked,
-                                    uint64_t *out) nogil
-    void random_bounded_uint32_fill(bitgen_t *bitgen_state,
-                                    uint32_t off, uint32_t rng, np.npy_intp cnt,
-                                    bint use_masked,
-                                    uint32_t *out) nogil
-    void random_bounded_uint16_fill(bitgen_t *bitgen_state,
-                                    uint16_t off, uint16_t rng, np.npy_intp cnt,
-                                    bint use_masked,
-                                    uint16_t *out) nogil
-    void random_bounded_uint8_fill(bitgen_t *bitgen_state,
-                                   uint8_t off, uint8_t rng, np.npy_intp cnt,
-                                   bint use_masked,
-                                   uint8_t *out) nogil
-    void random_bounded_bool_fill(bitgen_t *bitgen_state,
-                                  np.npy_bool off, np.npy_bool rng, np.npy_intp cnt,
-                                  bint use_masked,
-                                  np.npy_bool *out) nogil
-
-
-
-_integers_types = {'bool': (0, 2),
-                 'int8': (-2**7, 2**7),
-                 'int16': (-2**15, 2**15),
-                 'int32': (-2**31, 2**31),
-                 'int64': (-2**63, 2**63),
-                 'uint8': (0, 2**8),
-                 'uint16': (0, 2**16),
-                 'uint32': (0, 2**32),
-                 'uint64': (0, 2**64)}
-
-
-cdef object _rand_uint32_broadcast(np.ndarray low, np.ndarray high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for smaller integer types
-
-    This path is simpler since the high value in the open interval [low, high)
-    must be in-range for the next larger type, uint64. Here we case to
-    this type for checking and the recast to uint32 when producing the
-    random integers.
-    """
-    cdef uint32_t rng, last_rng, off, val, mask, out_val, is_open
-    cdef uint32_t buf
-    cdef uint32_t *out_data
-    cdef uint64_t low_v, high_v
-    cdef np.ndarray low_arr, high_arr, out_arr
-    cdef np.npy_intp i, cnt
-    cdef np.broadcast it
-    cdef int buf_rem = 0
-
-    # Array path
-    is_open = not closed
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, 0)):
-        raise ValueError('low is out of bounds for uint32')
-    if closed:
-        high_comp = np.greater_equal
-        low_high_comp = np.greater
-    else:
-        high_comp = np.greater
-        low_high_comp = np.greater_equal
-
-    if np.any(high_comp(high_arr, 0X100000000ULL)):
-        raise ValueError('high is out of bounds for uint32')
-    if np.any(low_high_comp(low_arr, high_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.uint32)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.uint32)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <uint32_t *>np.PyArray_DATA(out_arr)
-    cnt = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(cnt):
-            low_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            rng = <uint32_t>((high_v - is_open) - low_v)
-            off = <uint32_t>(<uint64_t>low_v)
-
-            if rng != last_rng:
-                # Smallest bit mask >= max
-                mask = <uint32_t>_gen_mask(rng)
-
-            out_data[i] = random_buffered_bounded_uint32(state, off, rng, mask, use_masked, &buf_rem, &buf)
-
-            np.PyArray_MultiIter_NEXT(it)
-    return out_arr
-
-cdef object _rand_uint16_broadcast(np.ndarray low, np.ndarray high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for smaller integer types
-
-    This path is simpler since the high value in the open interval [low, high)
-    must be in-range for the next larger type, uint32. Here we case to
-    this type for checking and the recast to uint16 when producing the
-    random integers.
-    """
-    cdef uint16_t rng, last_rng, off, val, mask, out_val, is_open
-    cdef uint32_t buf
-    cdef uint16_t *out_data
-    cdef uint32_t low_v, high_v
-    cdef np.ndarray low_arr, high_arr, out_arr
-    cdef np.npy_intp i, cnt
-    cdef np.broadcast it
-    cdef int buf_rem = 0
-
-    # Array path
-    is_open = not closed
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, 0)):
-        raise ValueError('low is out of bounds for uint16')
-    if closed:
-        high_comp = np.greater_equal
-        low_high_comp = np.greater
-    else:
-        high_comp = np.greater
-        low_high_comp = np.greater_equal
-
-    if np.any(high_comp(high_arr, 0X10000UL)):
-        raise ValueError('high is out of bounds for uint16')
-    if np.any(low_high_comp(low_arr, high_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT32, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_UINT32, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.uint16)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.uint16)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <uint16_t *>np.PyArray_DATA(out_arr)
-    cnt = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(cnt):
-            low_v = (<uint32_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<uint32_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            rng = <uint16_t>((high_v - is_open) - low_v)
-            off = <uint16_t>(<uint32_t>low_v)
-
-            if rng != last_rng:
-                # Smallest bit mask >= max
-                mask = <uint16_t>_gen_mask(rng)
-
-            out_data[i] = random_buffered_bounded_uint16(state, off, rng, mask, use_masked, &buf_rem, &buf)
-
-            np.PyArray_MultiIter_NEXT(it)
-    return out_arr
-
-cdef object _rand_uint8_broadcast(np.ndarray low, np.ndarray high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for smaller integer types
-
-    This path is simpler since the high value in the open interval [low, high)
-    must be in-range for the next larger type, uint16. Here we case to
-    this type for checking and the recast to uint8 when producing the
-    random integers.
-    """
-    cdef uint8_t rng, last_rng, off, val, mask, out_val, is_open
-    cdef uint32_t buf
-    cdef uint8_t *out_data
-    cdef uint16_t low_v, high_v
-    cdef np.ndarray low_arr, high_arr, out_arr
-    cdef np.npy_intp i, cnt
-    cdef np.broadcast it
-    cdef int buf_rem = 0
-
-    # Array path
-    is_open = not closed
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, 0)):
-        raise ValueError('low is out of bounds for uint8')
-    if closed:
-        high_comp = np.greater_equal
-        low_high_comp = np.greater
-    else:
-        high_comp = np.greater
-        low_high_comp = np.greater_equal
-
-    if np.any(high_comp(high_arr, 0X100UL)):
-        raise ValueError('high is out of bounds for uint8')
-    if np.any(low_high_comp(low_arr, high_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT16, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_UINT16, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.uint8)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.uint8)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <uint8_t *>np.PyArray_DATA(out_arr)
-    cnt = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(cnt):
-            low_v = (<uint16_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<uint16_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            rng = <uint8_t>((high_v - is_open) - low_v)
-            off = <uint8_t>(<uint16_t>low_v)
-
-            if rng != last_rng:
-                # Smallest bit mask >= max
-                mask = <uint8_t>_gen_mask(rng)
-
-            out_data[i] = random_buffered_bounded_uint8(state, off, rng, mask, use_masked, &buf_rem, &buf)
-
-            np.PyArray_MultiIter_NEXT(it)
-    return out_arr
-
-cdef object _rand_bool_broadcast(np.ndarray low, np.ndarray high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for smaller integer types
-
-    This path is simpler since the high value in the open interval [low, high)
-    must be in-range for the next larger type, uint8. Here we case to
-    this type for checking and the recast to bool when producing the
-    random integers.
-    """
-    cdef bool_t rng, last_rng, off, val, mask, out_val, is_open
-    cdef uint32_t buf
-    cdef bool_t *out_data
-    cdef uint8_t low_v, high_v
-    cdef np.ndarray low_arr, high_arr, out_arr
-    cdef np.npy_intp i, cnt
-    cdef np.broadcast it
-    cdef int buf_rem = 0
-
-    # Array path
-    is_open = not closed
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, 0)):
-        raise ValueError('low is out of bounds for bool')
-    if closed:
-        high_comp = np.greater_equal
-        low_high_comp = np.greater
-    else:
-        high_comp = np.greater
-        low_high_comp = np.greater_equal
-
-    if np.any(high_comp(high_arr, 0x2UL)):
-        raise ValueError('high is out of bounds for bool')
-    if np.any(low_high_comp(low_arr, high_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT8, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_UINT8, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.bool_)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.bool_)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <bool_t *>np.PyArray_DATA(out_arr)
-    cnt = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(cnt):
-            low_v = (<uint8_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<uint8_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            rng = <bool_t>((high_v - is_open) - low_v)
-            off = <bool_t>(<uint8_t>low_v)
-
-            if rng != last_rng:
-                # Smallest bit mask >= max
-                mask = <bool_t>_gen_mask(rng)
-
-            out_data[i] = random_buffered_bounded_bool(state, off, rng, mask, use_masked, &buf_rem, &buf)
-
-            np.PyArray_MultiIter_NEXT(it)
-    return out_arr
-
-cdef object _rand_int32_broadcast(np.ndarray low, np.ndarray high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for smaller integer types
-
-    This path is simpler since the high value in the open interval [low, high)
-    must be in-range for the next larger type, uint64. Here we case to
-    this type for checking and the recast to int32 when producing the
-    random integers.
-    """
-    cdef uint32_t rng, last_rng, off, val, mask, out_val, is_open
-    cdef uint32_t buf
-    cdef uint32_t *out_data
-    cdef uint64_t low_v, high_v
-    cdef np.ndarray low_arr, high_arr, out_arr
-    cdef np.npy_intp i, cnt
-    cdef np.broadcast it
-    cdef int buf_rem = 0
-
-    # Array path
-    is_open = not closed
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, -0x80000000LL)):
-        raise ValueError('low is out of bounds for int32')
-    if closed:
-        high_comp = np.greater_equal
-        low_high_comp = np.greater
-    else:
-        high_comp = np.greater
-        low_high_comp = np.greater_equal
-
-    if np.any(high_comp(high_arr, 0x80000000LL)):
-        raise ValueError('high is out of bounds for int32')
-    if np.any(low_high_comp(low_arr, high_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.int32)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.int32)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <uint32_t *>np.PyArray_DATA(out_arr)
-    cnt = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(cnt):
-            low_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            rng = <uint32_t>((high_v - is_open) - low_v)
-            off = <uint32_t>(<uint64_t>low_v)
-
-            if rng != last_rng:
-                # Smallest bit mask >= max
-                mask = <uint32_t>_gen_mask(rng)
-
-            out_data[i] = random_buffered_bounded_uint32(state, off, rng, mask, use_masked, &buf_rem, &buf)
-
-            np.PyArray_MultiIter_NEXT(it)
-    return out_arr
-
-cdef object _rand_int16_broadcast(np.ndarray low, np.ndarray high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for smaller integer types
-
-    This path is simpler since the high value in the open interval [low, high)
-    must be in-range for the next larger type, uint32. Here we case to
-    this type for checking and the recast to int16 when producing the
-    random integers.
-    """
-    cdef uint16_t rng, last_rng, off, val, mask, out_val, is_open
-    cdef uint32_t buf
-    cdef uint16_t *out_data
-    cdef uint32_t low_v, high_v
-    cdef np.ndarray low_arr, high_arr, out_arr
-    cdef np.npy_intp i, cnt
-    cdef np.broadcast it
-    cdef int buf_rem = 0
-
-    # Array path
-    is_open = not closed
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, -0x8000LL)):
-        raise ValueError('low is out of bounds for int16')
-    if closed:
-        high_comp = np.greater_equal
-        low_high_comp = np.greater
-    else:
-        high_comp = np.greater
-        low_high_comp = np.greater_equal
-
-    if np.any(high_comp(high_arr, 0x8000LL)):
-        raise ValueError('high is out of bounds for int16')
-    if np.any(low_high_comp(low_arr, high_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT32, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_INT32, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.int16)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.int16)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <uint16_t *>np.PyArray_DATA(out_arr)
-    cnt = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(cnt):
-            low_v = (<uint32_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<uint32_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            rng = <uint16_t>((high_v - is_open) - low_v)
-            off = <uint16_t>(<uint32_t>low_v)
-
-            if rng != last_rng:
-                # Smallest bit mask >= max
-                mask = <uint16_t>_gen_mask(rng)
-
-            out_data[i] = random_buffered_bounded_uint16(state, off, rng, mask, use_masked, &buf_rem, &buf)
-
-            np.PyArray_MultiIter_NEXT(it)
-    return out_arr
-
-cdef object _rand_int8_broadcast(np.ndarray low, np.ndarray high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for smaller integer types
-
-    This path is simpler since the high value in the open interval [low, high)
-    must be in-range for the next larger type, uint16. Here we case to
-    this type for checking and the recast to int8 when producing the
-    random integers.
-    """
-    cdef uint8_t rng, last_rng, off, val, mask, out_val, is_open
-    cdef uint32_t buf
-    cdef uint8_t *out_data
-    cdef uint16_t low_v, high_v
-    cdef np.ndarray low_arr, high_arr, out_arr
-    cdef np.npy_intp i, cnt
-    cdef np.broadcast it
-    cdef int buf_rem = 0
-
-    # Array path
-    is_open = not closed
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, -0x80LL)):
-        raise ValueError('low is out of bounds for int8')
-    if closed:
-        high_comp = np.greater_equal
-        low_high_comp = np.greater
-    else:
-        high_comp = np.greater
-        low_high_comp = np.greater_equal
-
-    if np.any(high_comp(high_arr, 0x80LL)):
-        raise ValueError('high is out of bounds for int8')
-    if np.any(low_high_comp(low_arr, high_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT16, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_INT16, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.int8)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.int8)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <uint8_t *>np.PyArray_DATA(out_arr)
-    cnt = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(cnt):
-            low_v = (<uint16_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<uint16_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            rng = <uint8_t>((high_v - is_open) - low_v)
-            off = <uint8_t>(<uint16_t>low_v)
-
-            if rng != last_rng:
-                # Smallest bit mask >= max
-                mask = <uint8_t>_gen_mask(rng)
-
-            out_data[i] = random_buffered_bounded_uint8(state, off, rng, mask, use_masked, &buf_rem, &buf)
-
-            np.PyArray_MultiIter_NEXT(it)
-    return out_arr
-
-
-cdef object _rand_uint64_broadcast(object low, object high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for 64-bit integer types
-
-    Requires special treatment since the high value can be out-of-range for
-    the largest (64 bit) integer type since the generator is specified on the
-    interval [low,high).
-
-    The internal generator does not have this issue since it generates from
-    the closes interval [low, high-1] and high-1 is always in range for the
-    64 bit integer type.
-    """
-
-    cdef np.ndarray low_arr, high_arr, out_arr, highm1_arr
-    cdef np.npy_intp i, cnt, n
-    cdef np.broadcast it
-    cdef object closed_upper
-    cdef uint64_t *out_data
-    cdef uint64_t *highm1_data
-    cdef uint64_t low_v, high_v
-    cdef uint64_t rng, last_rng, val, mask, off, out_val
-
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-
-    if np.any(np.less(low_arr, 0x0ULL)):
-        raise ValueError('low is out of bounds for uint64')
-    dt = high_arr.dtype
-    if closed or np.issubdtype(dt, np.integer):
-        # Avoid object dtype path if already an integer
-        high_lower_comp = np.less if closed else np.less_equal
-        if np.any(high_lower_comp(high_arr, 0x0ULL)):
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-        high_m1 = high_arr if closed else high_arr - dt.type(1)
-        if np.any(np.greater(high_m1, 0xFFFFFFFFFFFFFFFFULL)):
-            raise ValueError('high is out of bounds for uint64')
-        highm1_arr = <np.ndarray>np.PyArray_FROM_OTF(high_m1, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    else:
-        # If input is object or a floating type
-        highm1_arr = <np.ndarray>np.empty_like(high_arr, dtype=np.uint64)
-        highm1_data = <uint64_t *>np.PyArray_DATA(highm1_arr)
-        cnt = np.PyArray_SIZE(high_arr)
-        flat = high_arr.flat
-        for i in range(cnt):
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            closed_upper = int(flat[i]) - 1
-            if closed_upper > 0xFFFFFFFFFFFFFFFFULL:
-                raise ValueError('high is out of bounds for uint64')
-            if closed_upper < 0x0ULL:
-                comp = '>' if closed else '>='
-                raise ValueError('low {comp} high'.format(comp=comp))
-            highm1_data[i] = <uint64_t>closed_upper
-
-    if np.any(np.greater(low_arr, highm1_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    high_arr = highm1_arr
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.uint64)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.uint64)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <uint64_t *>np.PyArray_DATA(out_arr)
-    n = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(n):
-            low_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Generator produces values on the closed int [off, off+rng], -1 subtracted above
-            rng = <uint64_t>(high_v - low_v)
-            off = <uint64_t>(<uint64_t>low_v)
-
-            if rng != last_rng:
-                mask = _gen_mask(rng)
-            out_data[i] = random_bounded_uint64(state, off, rng, mask, use_masked)
-
-            np.PyArray_MultiIter_NEXT(it)
-
-    return out_arr
-
-cdef object _rand_int64_broadcast(object low, object high, object size,
-                                       bint use_masked, bint closed,
-                                       bitgen_t *state, object lock):
-    """
-    Array path for 64-bit integer types
-
-    Requires special treatment since the high value can be out-of-range for
-    the largest (64 bit) integer type since the generator is specified on the
-    interval [low,high).
-
-    The internal generator does not have this issue since it generates from
-    the closes interval [low, high-1] and high-1 is always in range for the
-    64 bit integer type.
-    """
-
-    cdef np.ndarray low_arr, high_arr, out_arr, highm1_arr
-    cdef np.npy_intp i, cnt, n
-    cdef np.broadcast it
-    cdef object closed_upper
-    cdef uint64_t *out_data
-    cdef int64_t *highm1_data
-    cdef int64_t low_v, high_v
-    cdef uint64_t rng, last_rng, val, mask, off, out_val
-
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
-
-    if np.any(np.less(low_arr, -0x8000000000000000LL)):
-        raise ValueError('low is out of bounds for int64')
-    dt = high_arr.dtype
-    if closed or np.issubdtype(dt, np.integer):
-        # Avoid object dtype path if already an integer
-        high_lower_comp = np.less if closed else np.less_equal
-        if np.any(high_lower_comp(high_arr, -0x8000000000000000LL)):
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-        high_m1 = high_arr if closed else high_arr - dt.type(1)
-        if np.any(np.greater(high_m1, 0x7FFFFFFFFFFFFFFFLL)):
-            raise ValueError('high is out of bounds for int64')
-        highm1_arr = <np.ndarray>np.PyArray_FROM_OTF(high_m1, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
-    else:
-        # If input is object or a floating type
-        highm1_arr = <np.ndarray>np.empty_like(high_arr, dtype=np.int64)
-        highm1_data = <int64_t *>np.PyArray_DATA(highm1_arr)
-        cnt = np.PyArray_SIZE(high_arr)
-        flat = high_arr.flat
-        for i in range(cnt):
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            closed_upper = int(flat[i]) - 1
-            if closed_upper > 0x7FFFFFFFFFFFFFFFLL:
-                raise ValueError('high is out of bounds for int64')
-            if closed_upper < -0x8000000000000000LL:
-                comp = '>' if closed else '>='
-                raise ValueError('low {comp} high'.format(comp=comp))
-            highm1_data[i] = <int64_t>closed_upper
-
-    if np.any(np.greater(low_arr, highm1_arr)):
-        comp = '>' if closed else '>='
-        raise ValueError('low {comp} high'.format(comp=comp))
-
-    high_arr = highm1_arr
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
-
-    if size is not None:
-        out_arr = <np.ndarray>np.empty(size, np.int64)
-    else:
-        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
-        out_arr = <np.ndarray>np.empty(it.shape, np.int64)
-
-    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
-    out_data = <uint64_t *>np.PyArray_DATA(out_arr)
-    n = np.PyArray_SIZE(out_arr)
-    mask = last_rng = 0
-    with lock, nogil:
-        for i in range(n):
-            low_v = (<int64_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
-            high_v = (<int64_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Generator produces values on the closed int [off, off+rng], -1 subtracted above
-            rng = <uint64_t>(high_v - low_v)
-            off = <uint64_t>(<int64_t>low_v)
-
-            if rng != last_rng:
-                mask = _gen_mask(rng)
-            out_data[i] = random_bounded_uint64(state, off, rng, mask, use_masked)
-
-            np.PyArray_MultiIter_NEXT(it)
-
-    return out_arr
-
-
-cdef object _rand_uint64(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_uint64(low, high, size, use_masked, *state, lock)
-
-    Return random np.uint64 integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.uint64 type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.uint64
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for uint64. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef uint64_t rng, off, out_val
-    cdef uint64_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.uint64)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < 0x0ULL:
-            raise ValueError("low is out of bounds for uint64")
-        if high > 0xFFFFFFFFFFFFFFFFULL:
-            raise ValueError("high is out of bounds for uint64")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <uint64_t>(high - low)
-        off = <uint64_t>(<uint64_t>low)
-        if size is None:
-            with lock:
-                random_bounded_uint64_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.uint64(<uint64_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.uint64)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <uint64_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_uint64_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_uint64_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
-
-cdef object _rand_uint32(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_uint32(low, high, size, use_masked, *state, lock)
-
-    Return random np.uint32 integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.uint32 type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.uint32
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for uint32. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef uint32_t rng, off, out_val
-    cdef uint32_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.uint32)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < 0x0UL:
-            raise ValueError("low is out of bounds for uint32")
-        if high > 0XFFFFFFFFUL:
-            raise ValueError("high is out of bounds for uint32")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <uint32_t>(high - low)
-        off = <uint32_t>(<uint32_t>low)
-        if size is None:
-            with lock:
-                random_bounded_uint32_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.uint32(<uint32_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.uint32)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <uint32_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_uint32_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_uint32_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
-
-cdef object _rand_uint16(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_uint16(low, high, size, use_masked, *state, lock)
-
-    Return random np.uint16 integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.uint16 type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.uint16
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for uint16. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef uint16_t rng, off, out_val
-    cdef uint16_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.uint16)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < 0x0UL:
-            raise ValueError("low is out of bounds for uint16")
-        if high > 0XFFFFUL:
-            raise ValueError("high is out of bounds for uint16")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <uint16_t>(high - low)
-        off = <uint16_t>(<uint16_t>low)
-        if size is None:
-            with lock:
-                random_bounded_uint16_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.uint16(<uint16_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.uint16)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <uint16_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_uint16_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_uint16_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
-
-cdef object _rand_uint8(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_uint8(low, high, size, use_masked, *state, lock)
-
-    Return random np.uint8 integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.uint8 type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.uint8
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for uint8. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef uint8_t rng, off, out_val
-    cdef uint8_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.uint8)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < 0x0UL:
-            raise ValueError("low is out of bounds for uint8")
-        if high > 0XFFUL:
-            raise ValueError("high is out of bounds for uint8")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <uint8_t>(high - low)
-        off = <uint8_t>(<uint8_t>low)
-        if size is None:
-            with lock:
-                random_bounded_uint8_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.uint8(<uint8_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.uint8)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <uint8_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_uint8_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_uint8_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
-
-cdef object _rand_bool(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_bool(low, high, size, use_masked, *state, lock)
-
-    Return random np.bool integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.bool type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.bool
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for bool. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef bool_t rng, off, out_val
-    cdef bool_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.bool)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < 0x0UL:
-            raise ValueError("low is out of bounds for bool")
-        if high > 0x1UL:
-            raise ValueError("high is out of bounds for bool")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <bool_t>(high - low)
-        off = <bool_t>(<bool_t>low)
-        if size is None:
-            with lock:
-                random_bounded_bool_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.bool_(<bool_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.bool)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <bool_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_bool_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_bool_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
-
-cdef object _rand_int64(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_int64(low, high, size, use_masked, *state, lock)
-
-    Return random np.int64 integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.int64 type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.int64
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for uint64. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef uint64_t rng, off, out_val
-    cdef uint64_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.int64)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < -0x8000000000000000LL:
-            raise ValueError("low is out of bounds for int64")
-        if high > 0x7FFFFFFFFFFFFFFFL:
-            raise ValueError("high is out of bounds for int64")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <uint64_t>(high - low)
-        off = <uint64_t>(<int64_t>low)
-        if size is None:
-            with lock:
-                random_bounded_uint64_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.int64(<int64_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.int64)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <uint64_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_uint64_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_int64_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
-
-cdef object _rand_int32(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_int32(low, high, size, use_masked, *state, lock)
-
-    Return random np.int32 integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.int32 type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.int32
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for uint32. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef uint32_t rng, off, out_val
-    cdef uint32_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.int32)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < -0x80000000L:
-            raise ValueError("low is out of bounds for int32")
-        if high > 0x7FFFFFFFL:
-            raise ValueError("high is out of bounds for int32")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <uint32_t>(high - low)
-        off = <uint32_t>(<int32_t>low)
-        if size is None:
-            with lock:
-                random_bounded_uint32_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.int32(<int32_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.int32)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <uint32_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_uint32_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_int32_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
-
-cdef object _rand_int16(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_int16(low, high, size, use_masked, *state, lock)
-
-    Return random np.int16 integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.int16 type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.int16
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for uint16. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef uint16_t rng, off, out_val
-    cdef uint16_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.int16)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < -0x8000L:
-            raise ValueError("low is out of bounds for int16")
-        if high > 0x7FFFL:
-            raise ValueError("high is out of bounds for int16")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <uint16_t>(high - low)
-        off = <uint16_t>(<int16_t>low)
-        if size is None:
-            with lock:
-                random_bounded_uint16_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.int16(<int16_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.int16)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <uint16_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_uint16_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_int16_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
-
-cdef object _rand_int8(object low, object high, object size,
-                             bint use_masked, bint closed,
-                             bitgen_t *state, object lock):
-    """
-    _rand_int8(low, high, size, use_masked, *state, lock)
-
-    Return random np.int8 integers from `low` (inclusive) to `high` (exclusive).
-
-    Return random integers from the "discrete uniform" distribution in the
-    interval [`low`, `high`).  If `high` is None (the default),
-    then results are from [0, `low`). On entry the arguments are presumed
-    to have been validated for size and order for the np.int8 type.
-
-    Parameters
-    ----------
-    low : int or array-like
-        Lowest (signed) integer to be drawn from the distribution (unless
-        ``high=None``, in which case this parameter is the *highest* such
-        integer).
-    high : int or array-like
-        If provided, one above the largest (signed) integer to be drawn from the
-        distribution (see above for behavior if ``high=None``).
-    size : int or tuple of ints
-        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
-        ``m * n * k`` samples are drawn.  Default is None, in which case a
-        single value is returned.
-    use_masked : bool
-        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
-    closed : bool
-        If True then sample from [low, high].  If False, sample [low, high)
-    state : bit generator
-        Bit generator state to use in the core random number generators
-    lock : threading.Lock
-        Lock to prevent multiple using a single generator simultaneously
-
-    Returns
-    -------
-    out : python scalar or ndarray of np.int8
-          `size`-shaped array of random integers from the appropriate
-          distribution, or a single such random int if `size` not provided.
-
-    Notes
-    -----
-    The internal integer generator produces values from the closed
-    interval [low, high-(not closed)].  This requires some care since
-    high can be out-of-range for uint8. The scalar path leaves
-    integers as Python integers until the 1 has been subtracted to
-    avoid needing to cast to a larger type.
-    """
-    cdef np.ndarray out_arr, low_arr, high_arr
-    cdef uint8_t rng, off, out_val
-    cdef uint8_t *out_data
-    cdef np.npy_intp i, n, cnt
-
-    if size is not None:
-        if (np.prod(size) == 0):
-            return np.empty(size, dtype=np.int8)
-
-    low_arr = <np.ndarray>np.array(low, copy=False)
-    high_arr = <np.ndarray>np.array(high, copy=False)
-    low_ndim = np.PyArray_NDIM(low_arr)
-    high_ndim = np.PyArray_NDIM(high_arr)
-    if ((low_ndim == 0 or (low_ndim == 1 and low_arr.size == 1 and size is not None)) and
-            (high_ndim == 0 or (high_ndim == 1 and high_arr.size == 1 and size is not None))):
-        low = int(low_arr)
-        high = int(high_arr)
-        # Subtract 1 since internal generator produces on closed interval [low, high]
-        if not closed:
-            high -= 1
-
-        if low < -0x80L:
-            raise ValueError("low is out of bounds for int8")
-        if high > 0x7FL:
-            raise ValueError("high is out of bounds for int8")
-        if low > high:  # -1 already subtracted, closed interval
-            comp = '>' if closed else '>='
-            raise ValueError('low {comp} high'.format(comp=comp))
-
-        rng = <uint8_t>(high - low)
-        off = <uint8_t>(<int8_t>low)
-        if size is None:
-            with lock:
-                random_bounded_uint8_fill(state, off, rng, 1, use_masked, &out_val)
-            return np.int8(<int8_t>out_val)
-        else:
-            out_arr = <np.ndarray>np.empty(size, np.int8)
-            cnt = np.PyArray_SIZE(out_arr)
-            out_data = <uint8_t *>np.PyArray_DATA(out_arr)
-            with lock, nogil:
-                random_bounded_uint8_fill(state, off, rng, cnt, use_masked, out_data)
-            return out_arr
-    return _rand_int8_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)