Proof of Neyman Pearson Lemma

I am trying to understand the proof of Neyman Pearson Lemma as Uniformly Most Powerful test from here (Page 3).

It says the following:

Let $$H_0: \theta = \theta_0$$ and $$H_a: \theta = \theta_1$$. Also, let the test-statistic of NP be $$\Lambda(x)$$ and that of a different test be $$K(x)$$. For a given $$\alpha$$ level, let $$c$$ and $$d$$ be the critical values for $$\Lambda(x)$$ and $$K(x)$$ respectively.

Let \begin{align} & \text{A = [the likelihood-ratio test rejects H_0 and the other test does not],} \\ & \text{B = [the other test rejects H_0 and the likelihood-ratio test does not],} \\ \text{and } & \text{C = [both tests reject H_0]}\end{align}

It offers an simple proof as the following:

\begin{align} & \text{Pr}(A|H_1) = \int_A f(x | \theta = \theta_1) dx \gt \int_A c f(x | \theta = \theta_0) dx = c\text{Pr}(A|H_0) \\ \overset{?}{= } & \, \, c\text{Pr}(B|H_0) = \int_B c f(x | \theta = \theta_0) dx \gt \int_B f(x | \theta = \theta_1) dx = \text{Pr}(B|H_1)\end{align}

I am unable to see why the questionmarked equality is valid.

Both tests have the same significance level; assuming that they both attain that level exactly:

$$P(\text{LRT rejects}|H_0)=P(\text{other test rejects}|H_0)=\alpha$$

$$\therefore P(\text{LRT rejects & other doesn't}|H_0)+P(\text{both reject}|H_0)\\\qquad=P(\text{other test rejects & LRT doesn't}|H_0)+P(\text{both reject}|H_0)$$

$$\text{or } P(\text{LRT rejects & other doesn't}|H_0)=P(\text{other test rejects & LRT doesn't}|H_0)$$