MLPY/Lib/site-packages/gym/vector/utils/numpy_utils.py

"""Numpy utility functions: concatenate space samples and create empty array."""
from collections import OrderedDict
from functools import singledispatch
from typing import Iterable, Union

import numpy as np

from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete, Space, Tuple

__all__ = ["concatenate", "create_empty_array"]


@singledispatch
def concatenate(
    space: Space, items: Iterable, out: Union[tuple, dict, np.ndarray]
) -> Union[tuple, dict, np.ndarray]:
    """Concatenate multiple samples from space into a single object.

    Example::

        >>> from gym.spaces import Box
        >>> space = Box(low=0, high=1, shape=(3,), dtype=np.float32)
        >>> out = np.zeros((2, 3), dtype=np.float32)
        >>> items = [space.sample() for _ in range(2)]
        >>> concatenate(space, items, out)
        array([[0.6348213 , 0.28607962, 0.60760117],
               [0.87383074, 0.192658  , 0.2148103 ]], dtype=float32)

    Args:
        space: Observation space of a single environment in the vectorized environment.
        items: Samples to be concatenated.
        out: The output object. This object is a (possibly nested) numpy array.

    Returns:
        The output object. This object is a (possibly nested) numpy array.

    Raises:
        ValueError: Space is not a valid :class:`gym.Space` instance
    """
    raise ValueError(
        f"Space of type `{type(space)}` is not a valid `gym.Space` instance."
    )


@concatenate.register(Box)
@concatenate.register(Discrete)
@concatenate.register(MultiDiscrete)
@concatenate.register(MultiBinary)
def _concatenate_base(space, items, out):
    return np.stack(items, axis=0, out=out)


@concatenate.register(Tuple)
def _concatenate_tuple(space, items, out):
    return tuple(
        concatenate(subspace, [item[i] for item in items], out[i])
        for (i, subspace) in enumerate(space.spaces)
    )


@concatenate.register(Dict)
def _concatenate_dict(space, items, out):
    return OrderedDict(
        [
            (key, concatenate(subspace, [item[key] for item in items], out[key]))
            for (key, subspace) in space.spaces.items()
        ]
    )


@concatenate.register(Space)
def _concatenate_custom(space, items, out):
    return tuple(items)


@singledispatch
def create_empty_array(
    space: Space, n: int = 1, fn: callable = np.zeros
) -> Union[tuple, dict, np.ndarray]:
    """Create an empty (possibly nested) numpy array.

    Example::

        >>> from gym.spaces import Box, Dict
        >>> space = Dict({
        ... 'position': Box(low=0, high=1, shape=(3,), dtype=np.float32),
        ... 'velocity': Box(low=0, high=1, shape=(2,), dtype=np.float32)})
        >>> create_empty_array(space, n=2, fn=np.zeros)
        OrderedDict([('position', array([[0., 0., 0.],
                                         [0., 0., 0.]], dtype=float32)),
                     ('velocity', array([[0., 0.],
                                         [0., 0.]], dtype=float32))])

    Args:
        space: Observation space of a single environment in the vectorized environment.
        n: Number of environments in the vectorized environment. If `None`, creates an empty sample from `space`.
        fn: Function to apply when creating the empty numpy array. Examples of such functions are `np.empty` or `np.zeros`.

    Returns:
        The output object. This object is a (possibly nested) numpy array.

    Raises:
        ValueError: Space is not a valid :class:`gym.Space` instance
    """
    raise ValueError(
        f"Space of type `{type(space)}` is not a valid `gym.Space` instance."
    )


@create_empty_array.register(Box)
@create_empty_array.register(Discrete)
@create_empty_array.register(MultiDiscrete)
@create_empty_array.register(MultiBinary)
def _create_empty_array_base(space, n=1, fn=np.zeros):
    shape = space.shape if (n is None) else (n,) + space.shape
    return fn(shape, dtype=space.dtype)


@create_empty_array.register(Tuple)
def _create_empty_array_tuple(space, n=1, fn=np.zeros):
    return tuple(create_empty_array(subspace, n=n, fn=fn) for subspace in space.spaces)


@create_empty_array.register(Dict)
def _create_empty_array_dict(space, n=1, fn=np.zeros):
    return OrderedDict(
        [
            (key, create_empty_array(subspace, n=n, fn=fn))
            for (key, subspace) in space.spaces.items()
        ]
    )


@create_empty_array.register(Space)
def _create_empty_array_custom(space, n=1, fn=np.zeros):
    return None