## Problem D: Where are the Squares?

The Hough Transform (HT) is a method for detecting linear structures in images. It can be used to isolate features of a particular shape within an image. Commonly it is used in image processing to detect lines. Lines can be represented in Cartesian space using the equation

 y = mx + c
For computational purposes it is sometimes better to use Polar Coordinates in the equation

 y = ⎛⎝ − cos θ sin θ ⎞⎠ x+ ⎛⎝ r sin θ ⎞⎠
This can be rearranged in the form of a Hough Space (r, θ):

 r = x  cos θ+ y   sin θ
Using this equation every line can be described as a combination of (r, θ), where r is the length of the normal vector that connects the line to the origin and θ is the angle of vector r. In order to be able to represent all possible lines the angle varies in the interval [0°,180°[ (values in degrees, not radians) and r is negative whenever it is bellow the abscissa. Example:
Line 1: r1 = 10, θ1 = 30°
Line 2: r2 = −8, θ2 = 135°
Line 3: r3 = 25, θ3 = 0°
Line 4: r4 = 23, θ4 = 90°
Given an image with a set of detected lines in Hough Space (no repeated lines), it is possible to detect all squares in the image as seen in the example:

Given an image with width W, height H and a set of L detected infinite straight lines, return the number of regular squares S in which the center of the square is inside the image. This means that for each square center (cx,cy) the following conditions apply: 0 ≤ cx < W and 0 ≤ cy < H.

### Input

The first line consists of three integers, W and H the width and height of the image and L, the number of lines. Then L lines follow, in each line there are two integers ri and θi, representing the Hough parameters of each line.

### Output

The output consist of a single line containing S, the number of squares with center inside the image.

### Constraints

• 0 ≤ W,H ≤ 2000
• 0 ≤ θi < 180
• 0 < L ≤ 1000

### Input example 1

```100 100 6
42 83
5 173
54 83
33 82
27 68
-7 173

```

```1

```