Distribution distinguishability as a decision problem

In the definition of a pseudorandom function, we consider two distributions $D_0$ and $D_1$ over functions, where $D_0$ is the distribution of a random function and $D_1$ is the distribution of a pseudorandom function (defined as the distribution of $F_k$ under uniform $k$ for some public function $F$). The function $F\sim D_1$ is pseudorandom if no probabilistic polynomial time (PPT) machine can distinguish it from $F\sim D_0$. More formally, two oracle distributions $D_0$ and $D_1$ are computationally indistinguishable if for every PPT distinguisher $M$, $$\left|\Pr_{O\sim D_0}[M^O(1^n)=1]-\Pr_{O\sim D_1}[M^O(1^n)=1]\right|=n^{-\omega(1)}.$$

I think that this definition can be phrased as an oracular language not in $\mathsf{BPP}$, but I am not sure how to do it. Hence my question is: Can we define a language $L^A$ which is not in $\mathsf{BPP}^A$ for some oracle $A$ iff $D_0$ and $D_1$ are computationally indistinguishable for any PPT machine?

As mentioned in this paper by Bennett and Gill, relative to a random oracle $A$, we can define a language $L^A=\{1^n:\text{the first $2^n$ bits of $A$ have $n$ consecutive zeros}\}$ and clearly $L^A\notin\mathsf{P}^A$. I am not sure how to solve my question because it refers to two distributions over functions.

Here's a candidate. Let's define $A=\{A_n:\{0,1\}^n\to\{0,1,\}^n\}_{n\in\mathbb{N}}$ to be a "hybrid" oracle, i.e., the $n$-th oracle $A_n$ is sampled either from $D_{0,n}$ or $D_{1,n}$ with probability $1/2$. The language $L^A$ is now defined as $$L^A:=\{1^n:A_n \text{ is pseudorandom}\}.$$ We claim that $L^A\notin\mathbf{BPP}^A$. Suppose for contradiction that it is, and let $\mathsf{D}^A$ be the PPT machine that decides $L^A$ in an infinitely-often manner. By definition of $A$, $\mathsf{D}^A$ can distinguish between $D_0$ and $D_1$ infinitely-often and hence break the pseudorandomness of $F$.