import torch

# xxhash primes converted to int64
XXPRIME_1 = -(11400714785074694791 ^ 0xffffffffffffffff) - 1
XXPRIME_2 = -(14029467366897019727 ^ 0xffffffffffffffff) - 1
XXPRIME_5 = 2870177450012600261
# python tuple value
XXPRIME_5_3527539 = XXPRIME_5 ^ 3527539

def tensor_python_tuple_hash(items, out_or_in_place):
    # https://github.com/python/cpython/blob/v3.13.2/Objects/tupleobject.c#L321
    # this is apparently a simplified & modified form of xxhash
    # learning xxhash could likely improve the code in this file

    len_ = len(items)
    # first iteration pulled out to provide storage placement
    if out_or_in_place is None:
        # in place
        acc = torch.add(XXPRIME_5, items[0], alpha=XXPRIME_2, out=items[0])
    else:
        # place in out_or_in_place
        acc = torch.add(XXPRIME_5, items[0], alpha=XXPRIME_2, out=out_or_in_place)
    # bitwise rotation
    upshift = acc << 31
    acc >>= 33
    acc &= 0x7fffffff # mask int64 sign extension
    acc |= upshift

    acc *= XXPRIME_1

    for i in range(1, len_):
        # acc += x * prime2
        acc.add_(items[i], alpha=XXPRIME_2)

        # bitwise rotation
        upshift = acc << 31
        acc >>= 33
        acc &= 0x7fffffff # mask int64 sign extension
        acc |= upshift

        acc *= XXPRIME_1

    acc += (len_ ^ XXPRIME_5_3527539)
    return acc

def hash(buffer, *incoming_unhashed_ints):
    if len(buffer) < 16:
        return bytes(buffer).__hash__()

    # first pass
    # - allocate storage
    # - place unhashed ints

    words = len(buffer) // 8
    dwords = words // 2
    incoming_data = torch.frombuffer(buffer, count=words, dtype=torch.int64)
    incoming_unhashed_ints = [int.from_bytes(buffer[words*8:],'little')] + list(incoming_unhashed_ints)

    incoming_hashable_length = words & ~1
    incoming_unhashed_length = words & 1
    incoming_unhashed_int_length = len(incoming_unhashed_ints)
    incoming_hashable_data = incoming_data[:incoming_hashable_length].view(2,-1)
    incoming_unhashed_data = incoming_data[incoming_hashable_length:]

    storage = torch.empty([dwords + incoming_unhashed_length + incoming_unhashed_int_length], dtype=torch.int64)
    hashed_data = tensor_python_tuple_hash(incoming_hashable_data, out_or_in_place=storage[:dwords])
    storage[dwords:-incoming_unhashed_int_length] = incoming_unhashed_data
    for idx in range(incoming_unhashed_int_length):
        storage[dwords+idx] = incoming_unhashed_ints[idx]

    incoming_data = storage
    words = len(incoming_data)
    dwords = words // 2

    # iterative passes
    while words > 1:
        incoming_hashable_length = words & ~1
        incoming_unhashed_length = words & 1
        incoming_hashable_data = incoming_data[:incoming_hashable_length].view(2,-1)
        incoming_unhashed_tensor_data = incoming_data[incoming_hashable_length:]

        hashed_data = tensor_python_tuple_hash(incoming_hashable_data, out_or_in_place=None)
        words = dwords + incoming_unhashed_length
        storage[dwords:words] = incoming_unhashed_tensor_data
        incoming_data = storage[:words]
        dwords = words // 2
        
    return incoming_data[0].item()

if __name__ == '__main__':
    print(hash(bytearray(b'the quick brown fox jumped over the lazy dog')))