"""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 https://github.com/lebedov/msgpack-numpy. 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)
