mistral-7b-instruct-v0.2.Q8_0.gguf

---

# Task path
/arc-dataset-collection/dataset/arc-dataset-diva/data/port_cw/port_cw_93_75_46_55_80.json

# Task json
{'train': [{'input': [[9], [3]], 'output': [[3, 9]]}, {'input': [[7], [5]], 'output': [[5, 7]]}], 'test': [{'input': [[4], [6]], 'output': [[6, 4]]}, {'input': [[5], [5]], 'output': [[5, 5]]}, {'input': [[8], [0]], 'output': [[0, 8]]}]}



# Misc
model=local-model
temperature=0.0
token_limit=4096


# System content:
You solve puzzles by transforming the input into the output. You are expert at the PGM (Portable Graymap) format.


# User content: 722 bytes
# Transformations

## Transformation A - Input

```pgm
P2
# input
1 2
9
9
3
```

## Transformation A - Output

```pgm
P2
# output
2 1
9
3 9
```

## Transformation B - Input

```pgm
P2
# input
1 2
9
7
5
```

## Transformation B - Output

```pgm
P2
# output
2 1
9
5 7
```

## Transformation C - Input

```pgm
P2
# input
1 2
9
4
6
```

## Transformation C - Output

```pgm
P2
# predict this output
```

# Task A: Analysis

Think step by step, what does the transformation have in common. Use max 100 words.

Explain how to compute width and height. Use max 10 words.

# Task B: Solution

I want you to solve `Transformation C - Output`.

# Task C: Verify

Double check that your reasoning is correct and check the solution.




---

 The transformations have in common that they rearrange the pixels of the image based on their intensity values, with the goal of having all pixels with the same intensity value grouped together horizontally. This results in a compressed representation of the image where rows with identical pixel intensities are merged into a single row.

To compute the width and height of an image, we can look at the number of columns (width) and rows (height) specified in the PGM header. For example, if the first line of the PGM file reads "P5 100 100", then the width is 100 and the height is 100.

To solve Transformation C - Output, we can follow the same logic as in the previous transformations:

1. Sort all pixels in ascending order based on their intensity values.
2. Group adjacent pixels with the same intensity value into a single pixel.
3. Determine the number of unique intensity values and assign each group a new intensity value, starting from 0.
4. Write the resulting image with the new intensity values to an output file.

Based on this logic, we can predict that the output image will have the following pixels:

```pgm
P2
# output
2 1
9
6 4
```

---



Correct!

