import numpy as np
from .space import Space


class MultiBinary(Space):
    '''
    An n-dimensional binary space. 

    The argument to MultiBinary defines n.
    
    Example Usage:
    
    >> self.observation_space = spaces.MultiBinary(5)

    >> self.observation_space.sample()

        array([0,1,0,1,0], dtype =int8)

    '''
    
    def __init__(self, n):
        self.n = n
        super(MultiBinary, self).__init__((self.n,), np.int8)

    def sample(self):
        return self.np_random.randint(low=0, high=2, size=self.n, dtype=self.dtype)

    def contains(self, x):
        if isinstance(x, list):
            x = np.array(x)  # Promote list to array for contains check
        return ((x==0) | (x==1)).all()

    def to_jsonable(self, sample_n):
        return np.array(sample_n).tolist()

    def from_jsonable(self, sample_n):
        return [np.asarray(sample) for sample in sample_n]

    def __repr__(self):
        return "MultiBinary({})".format(self.n)

    def __eq__(self, other):
        return isinstance(other, MultiBinary) and self.n == other.n

import numpy as np
from .space import Space


class MultiBinary(Space):
    '''
    An n-shape binary space. 

    The argument to MultiBinary defines n, which could be a number or a `list` of numbers.
    
    Example Usage:
    
    >> self.observation_space = spaces.MultiBinary(5)

    >> self.observation_space.sample()

        array([0,1,0,1,0], dtype =int8)

    >> self.observation_space = spaces.MultiBinary([3,2])

    >> self.observation_space.sample()

        array([[0, 0],
               [0, 1],   
               [1, 1]], dtype=int8)

    '''
    def __init__(self, n):
        self.n = n
        if type(n) in [tuple, list, np.ndarray]:
            input_n = n
        else:
            input_n = (n, )
        super(MultiBinary, self).__init__(input_n, np.int8)

    def sample(self):
        return self.np_random.randint(low=0, high=2, size=self.n, dtype=self.dtype)

    def contains(self, x):
        if isinstance(x, list) or isinstance(x, tuple):
            x = np.array(x)  # Promote list to array for contains check
        if self.shape != x.shape:
            return False
        return ((x==0) | (x==1)).all()

    def to_jsonable(self, sample_n):
        return np.array(sample_n).tolist()

    def from_jsonable(self, sample_n):
        return [np.asarray(sample) for sample in sample_n]

    def __repr__(self):
        return "MultiBinary({})".format(self.n)

    def __eq__(self, other):
        return isinstance(other, MultiBinary) and self.n == other.n

Add support for tuple input on MultiBinary space (#2023)