# Translated from the reference implementation # at https://github.com/veorq/SipHash import cython from libc.stdlib cimport ( free, malloc, ) import numpy as np from numpy cimport ( import_array, ndarray, uint8_t, uint32_t, uint64_t, ) import_array() from pandas._libs.util cimport is_nan DEF cROUNDS = 2 DEF dROUNDS = 4 @cython.boundscheck(False) def hash_object_array( ndarray[object] arr, str key, str encoding="utf8" ) -> np.ndarray[np.uint64]: """ Parameters ---------- arr : 1-d object ndarray of objects key : hash key, must be 16 byte len encoded encoding : encoding for key & arr, default to 'utf8' Returns ------- 1-d uint64 ndarray of hashes. Raises ------ TypeError If the array contains mixed types. Notes ----- Allowed values must be strings, or nulls mixed array types will raise TypeError. """ cdef: Py_ssize_t i, l, n uint64_t[:] result bytes data, k uint8_t *kb uint64_t *lens char **vecs char *cdata object val list datas = [] k = key.encode(encoding) kb = k if len(k) != 16: raise ValueError( f"key should be a 16-byte string encoded, got {k} (len {len(k)})" ) n = len(arr) # create an array of bytes vecs = malloc(n * sizeof(char *)) lens = malloc(n * sizeof(uint64_t)) for i in range(n): val = arr[i] if isinstance(val, bytes): data = val elif isinstance(val, str): data = val.encode(encoding) elif val is None or is_nan(val): # null, stringify and encode data = str(val).encode(encoding) elif isinstance(val, tuple): # GH#28969 we could have a tuple, but need to ensure that # the tuple entries are themselves hashable before converting # to str hash(val) data = str(val).encode(encoding) else: raise TypeError( f"{val} of type {type(val)} is not a valid type for hashing, " "must be string or null" ) l = len(data) lens[i] = l cdata = data # keep the references alive through the end of the # function datas.append(data) vecs[i] = cdata result = np.empty(n, dtype=np.uint64) with nogil: for i in range(n): result[i] = low_level_siphash(vecs[i], lens[i], kb) free(vecs) free(lens) return result.base # .base to retrieve underlying np.ndarray cdef inline uint64_t _rotl(uint64_t x, uint64_t b) nogil: return (x << b) | (x >> (64 - b)) cdef inline void u32to8_le(uint8_t* p, uint32_t v) nogil: p[0] = (v) p[1] = (v >> 8) p[2] = (v >> 16) p[3] = (v >> 24) cdef inline uint64_t u8to64_le(uint8_t* p) nogil: return (p[0] | p[1] << 8 | p[2] << 16 | p[3] << 24 | p[4] << 32 | p[5] << 40 | p[6] << 48 | p[7] << 56) cdef inline void _sipround(uint64_t* v0, uint64_t* v1, uint64_t* v2, uint64_t* v3) nogil: v0[0] += v1[0] v1[0] = _rotl(v1[0], 13) v1[0] ^= v0[0] v0[0] = _rotl(v0[0], 32) v2[0] += v3[0] v3[0] = _rotl(v3[0], 16) v3[0] ^= v2[0] v0[0] += v3[0] v3[0] = _rotl(v3[0], 21) v3[0] ^= v0[0] v2[0] += v1[0] v1[0] = _rotl(v1[0], 17) v1[0] ^= v2[0] v2[0] = _rotl(v2[0], 32) @cython.cdivision(True) cdef uint64_t low_level_siphash(uint8_t* data, size_t datalen, uint8_t* key) nogil: cdef uint64_t v0 = 0x736f6d6570736575ULL cdef uint64_t v1 = 0x646f72616e646f6dULL cdef uint64_t v2 = 0x6c7967656e657261ULL cdef uint64_t v3 = 0x7465646279746573ULL cdef uint64_t b cdef uint64_t k0 = u8to64_le(key) cdef uint64_t k1 = u8to64_le(key + 8) cdef uint64_t m cdef int i cdef uint8_t* end = data + datalen - (datalen % sizeof(uint64_t)) cdef int left = datalen & 7 cdef int left_byte b = (datalen) << 56 v3 ^= k1 v2 ^= k0 v1 ^= k1 v0 ^= k0 while (data != end): m = u8to64_le(data) v3 ^= m for i in range(cROUNDS): _sipround(&v0, &v1, &v2, &v3) v0 ^= m data += sizeof(uint64_t) for i in range(left-1, -1, -1): b |= (data[i]) << (i * 8) v3 ^= b for i in range(cROUNDS): _sipround(&v0, &v1, &v2, &v3) v0 ^= b v2 ^= 0xff for i in range(dROUNDS): _sipround(&v0, &v1, &v2, &v3) b = v0 ^ v1 ^ v2 ^ v3 return b