Docker Setup

I have setup the docker container to run the model evaluation.

Initial Query

Write a self-contained Python program that prints a JSON output of how often each word appears in a text file located in /tmp/file.txt. So if the file contains "This is is is, an example" then the program should print to stdout. {"This": 1, "is": 2, "is,":1, "an":1, "example":1} The program should be run by calling /path/to/python_word_count with no arguments. Make it run in parallel on 4 cores at the same time, each taking 1/4 of the file. Print out 4 differnt json dicts, one per line. I'll do the merging.

LLM Generation

Query

Output

Sure! Below is a self-contained Python program that counts the frequency of each word in `/tmp/file.txt` using 4 parallel processes. Each process handles a portion of the file, and the program outputs four separate JSON dictionaries, one per line.

import json
from multiprocessing import Pool, cpu_count

def count_words(lines):
    """Count word frequencies in a list of lines."""
    counts = {}
    for line in lines:
        words = line.split()
        for word in words:
            counts[word] = counts.get(word, 0) + 1
    return counts

def split_lines(lines, n):
    """Split lines into n roughly equal parts."""
    k, m = divmod(len(lines), n)
    return [lines[i*k + min(i, m):(i+1)*k + min(i+1, m)] for i in range(n)]

def main():
    file_path = "/tmp/file.txt"
    num_processes = 4  # Number of parallel processes

    # Read all lines from the file
    try:
        with open(file_path, "r", encoding="utf-8") as f:
            lines = f.readlines()
    except FileNotFoundError:
        print(f"File not found: {file_path}")
        return
    except Exception as e:
        print(f"Error reading file: {e}")
        return

    # Split lines into chunks for each process
    chunks = split_lines(lines, num_processes)

    # Create a pool of worker processes
    with Pool(processes=num_processes) as pool:
        # Map the count_words function to each chunk
        results = pool.map(count_words, chunks)

    # Output each dictionary as a JSON string on a separate line
    for word_count in results:
        print(json.dumps(word_count))

if __name__ == "__main__":
    main()

### How It Works: 1. **Reading the File:** - The program reads all lines from `/tmp/file.txt`. It handles potential file reading errors gracefully. 2. **Splitting the Work:** - The `split_lines` function divides the list of lines into 4 roughly equal parts, ensuring that each process gets an approximately equal portion of the file to work on. 3. **Counting Words in Parallel:** - Using Python's `multiprocessing.Pool`, the program spawns 4 parallel processes. - Each process executes the `count_words` function on its assigned chunk of lines, counting the frequency of each word. 4. **Outputting the Results:** - After all processes complete, the main process collects the four dictionaries. - Each dictionary is converted to a JSON string and printed on a separate line to `stdout`. ### Example: Given the content of `/tmp/file.txt` as:

This is is is, an example

Running the program will output something like:

{"This": 1}
{}
{}
{"is": 2, "is,": 1, "an": 1, "example": 1}

Each JSON object corresponds to one of the four parallel processes. You can merge these dictionaries as needed. ### Running the Program: Save the script to a file, for example, `python_word_count.py`, and ensure it has execute permissions. You can run it using:

1	python /path/to/python_word_count.py

Ensure that the Python interpreter you're using is compatible with the script (Python 3 is recommended).

Extract Code

I extracted the following code from that output:

import json
from multiprocessing import Pool, cpu_count

def count_words(lines):
    """Count word frequencies in a list of lines."""
    counts = {}
    for line in lines:
        words = line.split()
        for word in words:
            counts[word] = counts.get(word, 0) + 1
    return counts

def split_lines(lines, n):
    """Split lines into n roughly equal parts."""
    k, m = divmod(len(lines), n)
    return [lines[i*k + min(i, m):(i+1)*k + min(i+1, m)] for i in range(n)]

def main():
    file_path = "/tmp/file.txt"
    num_processes = 4  # Number of parallel processes

    # Read all lines from the file
    try:
        with open(file_path, "r", encoding="utf-8") as f:
            lines = f.readlines()
    except FileNotFoundError:
        print(f"File not found: {file_path}")
        return
    except Exception as e:
        print(f"Error reading file: {e}")
        return

    # Split lines into chunks for each process
    chunks = split_lines(lines, num_processes)

    # Create a pool of worker processes
    with Pool(processes=num_processes) as pool:
        # Map the count_words function to each chunk
        results = pool.map(count_words, chunks)

    # Output each dictionary as a JSON string on a separate line
    for word_count in results:
        print(json.dumps(word_count))

if __name__ == "__main__":
    main()

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. import json from multiprocessing import Pool, cpu_count def count_words(lines): """Count word frequencies in a list of lines.""" counts = {} for line in lines: words = line.split() for word in words: counts[word] = counts.get(word, 0) + 1 return counts def split_lines(lines, n): """Split lines into n roughly equal parts.""" k, m = divmod(len(lines), n) return [lines[i*k + min(i, m):(i+1)*k + min(i+1, m)] for i in range(n)] def main(): file_path = "/tmp/file.txt" num_processes = 4 # Number of parallel processes # Read all lines from the file try: with open(file_path, "r", encoding="utf-8") as f: lines = f.readlines() except FileNotFoundError: print(f"File not found: {file_path}") return except Exception as e: print(f"Error reading file: {e}") return # Split lines into chunks for each process chunks = split_lines(lines, num_processes) # Create a pool of worker processes with Pool(processes=num_processes) as pool: # Map the count_words function to each chunk results = pool.map(count_words, chunks) # Output each dictionary as a JSON string on a separate line for word_count in results: print(json.dumps(word_count)) if __name__ == "__main__": main() Answer either 'The student passes' or 'The student fails'.

Output

The student passes.

Substring Evaluation

Testing if the previous output contains the string student passes: True

Run Code Interpreter

Running the following program:

import json
from multiprocessing import Pool, cpu_count

def count_words(lines):
    """Count word frequencies in a list of lines."""
    counts = {}
    for line in lines:
        words = line.split()
        for word in words:
            counts[word] = counts.get(word, 0) + 1
    return counts

def split_lines(lines, n):
    """Split lines into n roughly equal parts."""
    k, m = divmod(len(lines), n)
    return [lines[i*k + min(i, m):(i+1)*k + min(i+1, m)] for i in range(n)]

def main():
    file_path = "/tmp/file.txt"
    num_processes = 4  # Number of parallel processes

    # Read all lines from the file
    try:
        with open(file_path, "r", encoding="utf-8") as f:
            lines = f.readlines()
    except FileNotFoundError:
        print(f"File not found: {file_path}")
        return
    except Exception as e:
        print(f"Error reading file: {e}")
        return

    # Split lines into chunks for each process
    chunks = split_lines(lines, num_processes)

    # Create a pool of worker processes
    with Pool(processes=num_processes) as pool:
        # Map the count_words function to each chunk
        results = pool.map(count_words, chunks)

    # Output each dictionary as a JSON string on a separate line
    for word_count in results:
        print(json.dumps(word_count))

if __name__ == "__main__":
    main()

And got the output:

{"it": 10, "was": 10, "the": 11, "best": 1, "of": 10, "times,": 2, "worst": 1, "age": 2, "wisdom,": 1, "foolishness,": 1, "epoch": 2, "belief,": 1, "incredulity,": 1, "season": 2, "Light,": 1, "Darkness,": 1, "spring": 1, "hope,": 1, "winter": 1, "despair,": 1, "we": 4, "had": 2, "everything": 1, "before": 2, "us,": 2, "nothing": 1, "were": 2, "all": 2, "going": 2, "direct": 2, "to": 1, "Heaven,": 1, "other": 1, "way": 1}
{}
{}
{}

PyFunc

Resulting in output: {'it': 10, 'was': 10, 'the': 11, 'best': 1, 'of': 10, 'times,': 2, 'worst': 1, 'age': 2, 'wisdom,': 1, 'foolishness,': 1, 'epoch': 2, 'belief,': 1, 'incredulity,': 1, 'season': 2, 'Light,': 1, 'Darkness,': 1, 'spring': 1, 'hope,': 1, 'winter': 1, 'despair,': 1, 'we': 4, 'had': 2, 'everything': 1, 'before': 2, 'us,': 2, 'nothing': 1, 'were': 2, 'all': 2, 'going': 2, 'direct': 2, 'to': 1, 'Heaven,': 1, 'other': 1, 'way': 1}

Equal Evaluation

Testing if the previous output equals the string {'the': 11, 'it': 10, 'was': 10, 'of': 10, 'we': 4, 'times,': 2, 'age': 2, 'epoch': 2, 'season': 2, 'had': 2, 'before': 2, 'us,': 2, 'were': 2, 'all': 2, 'going': 2, 'direct': 2, 'best': 1, 'worst': 1, 'wisdom,': 1, 'foolishness,': 1, 'belief,': 1, 'incredulity,': 1, 'Light,': 1, 'Darkness,': 1, 'spring': 1, 'hope,': 1, 'winter': 1, 'despair,': 1, 'everything': 1, 'nothing': 1, 'to': 1, 'Heaven,': 1, 'other': 1, 'way': 1}: True

Final Answer: True

Docker Setup

I have setup the docker container to run the model evaluation.

Initial Query

LLM Generation

Query

Output

#!/usr/bin/env python3

import json
import os
import multiprocessing

def count_words(text):
    counts = {}
    for word in text.split():
        counts[word] = counts.get(word, 0) + 1
    return counts

def split_file(filename, num_chunks):
    file_size = os.path.getsize(filename)
    chunk_sizes = []
    chunk_size = file_size // num_chunks
    positions = [0]

    with open(filename, 'rb') as f:
        for i in range(1, num_chunks):
            f.seek(i * chunk_size)
            f.readline()  # Move to the end of the current line
            positions.append(f.tell())
        positions.append(file_size)

    chunks = []
    with open(filename, 'r', encoding='utf-8') as f:
        for i in range(num_chunks):
            start = positions[i]
            end = positions[i + 1]
            f.seek(start)
            chunk = f.read(end - start)
            chunks.append(chunk)
    return chunks

def main():
    filename = '/tmp/file.txt'
    num_processes = 4

    chunks = split_file(filename, num_processes)

    with multiprocessing.Pool(processes=num_processes) as pool:
        results = pool.map(count_words, chunks)

    for result in results:
        print(json.dumps(result))

if __name__ == '__main__':
    main()

### Explanation: 1. **Shebang Line**: `#!/usr/bin/env python3` allows the script to be executed directly from the command line. 2. **Imports**: - `json` for JSON serialization. - `os` for file size determination. - `multiprocessing` for parallel processing. 3. **count_words Function**: - Takes a string `text` as input. - Splits the text into words based on whitespace. - Counts the occurrences of each word and returns a dictionary. 4. **split_file Function**: - Divides the file into `num_chunks` parts without splitting words. - Determines split positions by seeking to approximate byte positions and then moving to the end of the current line to avoid breaking words. - Reads each chunk as a string and collects them into a list. 5. **main Function**: - Defines the file path and number of processes. - Splits the file into chunks. - Creates a pool of worker processes. - Maps the `count_words` function to each chunk in parallel. - Prints each resulting JSON dictionary on a separate line. 6. **Execution Guard**: - Ensures that `main` is called only when the script is executed directly. ### Usage: 1. **Make the Script Executable**:

    chmod +x /path/to/python_word_count
    

2. **Run the Script**:

    /path/to/python_word_count
    

This script will output four JSON dictionaries, each representing the word counts from a portion of the file `/tmp/file.txt`. Each dictionary will be printed on a separate line, allowing you to merge them as needed. ### Notes: - **Word Splitting**: The `split_file` function ensures that words are not split between chunks by moving to the end of the current line. This assumes that words are separated by newlines or spaces. If your file does not contain newlines, you may need a more sophisticated method to prevent splitting words. - **Performance**: This script reads the file in chunks and processes them in parallel, leveraging multiple CPU cores for efficiency. - **Merging Results**: After running the script, you'll receive four separate JSON dictionaries. You can merge these dictionaries by summing the counts for each word. ### Example Output: Given the file contents `"This is is is, an example"`, running the script might produce:

{"This": 1, "is": 2}
{"is,": 1, "an": 1, "example": 1}
{}
{}

Each line represents the word counts from a different chunk of the file.

Extract Code

I extracted the following code from that output:

#!/usr/bin/env python3

import json
import os
import multiprocessing

def count_words(text):
    counts = {}
    for word in text.split():
        counts[word] = counts.get(word, 0) + 1
    return counts

def split_file(filename, num_chunks):
    file_size = os.path.getsize(filename)
    chunk_sizes = []
    chunk_size = file_size // num_chunks
    positions = [0]

    with open(filename, 'rb') as f:
        for i in range(1, num_chunks):
            f.seek(i * chunk_size)
            f.readline()  # Move to the end of the current line
            positions.append(f.tell())
        positions.append(file_size)

    chunks = []
    with open(filename, 'r', encoding='utf-8') as f:
        for i in range(num_chunks):
            start = positions[i]
            end = positions[i + 1]
            f.seek(start)
            chunk = f.read(end - start)
            chunks.append(chunk)
    return chunks

def main():
    filename = '/tmp/file.txt'
    num_processes = 4

    chunks = split_file(filename, num_processes)

    with multiprocessing.Pool(processes=num_processes) as pool:
        results = pool.map(count_words, chunks)

    for result in results:
        print(json.dumps(result))

if __name__ == '__main__':
    main()

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. #!/usr/bin/env python3 import json import os import multiprocessing def count_words(text): counts = {} for word in text.split(): counts[word] = counts.get(word, 0) + 1 return counts def split_file(filename, num_chunks): file_size = os.path.getsize(filename) chunk_sizes = [] chunk_size = file_size // num_chunks positions = [0] with open(filename, 'rb') as f: for i in range(1, num_chunks): f.seek(i * chunk_size) f.readline() # Move to the end of the current line positions.append(f.tell()) positions.append(file_size) chunks = [] with open(filename, 'r', encoding='utf-8') as f: for i in range(num_chunks): start = positions[i] end = positions[i + 1] f.seek(start) chunk = f.read(end - start) chunks.append(chunk) return chunks def main(): filename = '/tmp/file.txt' num_processes = 4 chunks = split_file(filename, num_processes) with multiprocessing.Pool(processes=num_processes) as pool: results = pool.map(count_words, chunks) for result in results: print(json.dumps(result)) if __name__ == '__main__': main() Answer either 'The student passes' or 'The student fails'.

Output

The student passes.

Substring Evaluation

Testing if the previous output contains the string student passes: True

Run Code Interpreter

Running the following program:

#!/usr/bin/env python3

import json
import os
import multiprocessing

def count_words(text):
    counts = {}
    for word in text.split():
        counts[word] = counts.get(word, 0) + 1
    return counts

def split_file(filename, num_chunks):
    file_size = os.path.getsize(filename)
    chunk_sizes = []
    chunk_size = file_size // num_chunks
    positions = [0]

    with open(filename, 'rb') as f:
        for i in range(1, num_chunks):
            f.seek(i * chunk_size)
            f.readline()  # Move to the end of the current line
            positions.append(f.tell())
        positions.append(file_size)

    chunks = []
    with open(filename, 'r', encoding='utf-8') as f:
        for i in range(num_chunks):
            start = positions[i]
            end = positions[i + 1]
            f.seek(start)
            chunk = f.read(end - start)
            chunks.append(chunk)
    return chunks

def main():
    filename = '/tmp/file.txt'
    num_processes = 4

    chunks = split_file(filename, num_processes)

    with multiprocessing.Pool(processes=num_processes) as pool:
        results = pool.map(count_words, chunks)

    for result in results:
        print(json.dumps(result))

if __name__ == '__main__':
    main()

And got the output:

{"it": 10, "was": 10, "the": 11, "best": 1, "of": 10, "times,": 2, "worst": 1, "age": 2, "wisdom,": 1, "foolishness,": 1, "epoch": 2, "belief,": 1, "incredulity,": 1, "season": 2, "Light,": 1, "Darkness,": 1, "spring": 1, "hope,": 1, "winter": 1, "despair,": 1, "we": 4, "had": 2, "everything": 1, "before": 2, "us,": 2, "nothing": 1, "were": 2, "all": 2, "going": 2, "direct": 2, "to": 1, "Heaven,": 1, "other": 1, "way": 1}
{}
{}
{}