"""Adds NumPy array support to msgpack.

msgpack is good for (de)serializing data over a network for multiple reasons:
- msgpack is secure (as opposed to pickle/dill/etc which allow for arbitrary code execution)
- msgpack is widely used and has good cross-language support
- msgpack does not require a schema (as opposed to protobuf/flatbuffers/etc) which is convenient in dynamically typed
    languages like Python and JavaScript
- msgpack is fast and efficient (as opposed to readable formats like JSON/YAML/etc); I found that msgpack was ~4x faster
    than pickle for serializing large arrays using the below strategy

The code below is adapted from GitHub - lebedov/msgpack-numpy: Serialize numpy arrays using msgpack. The reason not to use that library directly is
that it falls back to pickle for object arrays.
"""

import functools

import msgpack
import numpy as np


def pack_array(obj):
    if (isinstance(obj, (np.ndarray, np.generic))) and obj.dtype.kind in ("V", "O", "c"):
        raise ValueError(f"Unsupported dtype: {obj.dtype}")

    if isinstance(obj, np.ndarray):
        return {
            b"__ndarray__": True,
            b"data": obj.tobytes(),
            b"dtype": obj.dtype.str,
            b"shape": obj.shape,
        }

    if isinstance(obj, np.generic):
        return {
            b"__npgeneric__": True,
            b"data": obj.item(),
            b"dtype": obj.dtype.str,
        }

    return obj


def unpack_array(obj):
    if b"__ndarray__" in obj:
        return np.ndarray(buffer=obj[b"data"], dtype=np.dtype(obj[b"dtype"]), shape=obj[b"shape"])

    if b"__npgeneric__" in obj:
        return np.dtype(obj[b"dtype"]).type(obj[b"data"])

    return obj


Packer = functools.partial(msgpack.Packer, default=pack_array)
packb = functools.partial(msgpack.packb, default=pack_array)

Unpacker = functools.partial(msgpack.Unpacker, object_hook=unpack_array)
unpackb = functools.partial(msgpack.unpackb, object_hook=unpack_array)