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

Here's a Python script that uses the multiprocessing library to read and process the file in parallel. Each worker process will read a chunk of the file, count the word frequencies, and print out a JSON dictionary.

import os
import json
import multiprocessing as mp
from collections import defaultdict
from functools import partial

def word_count(chunk):
    words = chunk.split()
    freq = defaultdict(int)
    for word in words:
        freq[word] += 1
    return freq

def process_chunk(filepath, start, end):
    with open(filepath, 'r') as f:
        f.seek(start)
        chunk = f.read(end - start)
    word_freq = word_count(chunk)
    print(json.dumps(word_freq))

def main():
    input_file = '/tmp/file.txt'
    file_size = os.path.getsize(input_file)
    num_processes = mp.cpu_count()

    chunk_size = file_size // num_processes
    processes = []

    for i in range(num_processes):
        start = i * chunk_size
        end = start + chunk_size if i < num_processes - 1 else None
        p = mp.Process(target=process_chunk, args=(input_file, start, end))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()

if __name__ == '__main__':
    main()

Save the script as `python_word_count.py` and run it using the command `python3 python_word_count.py`. The script will print out 4 JSON dictionaries, one on each line. Keep in mind that this script only works well for large files because it splits the file into chunks without considering word boundaries. For smaller files or for accurate word counting, you may need a more sophisticated solution that takes word boundaries into account.

Extract Code

I extracted the following code from that output:

import os
import json
import multiprocessing as mp
from collections import defaultdict
from functools import partial

def word_count(chunk):
    words = chunk.split()
    freq = defaultdict(int)
    for word in words:
        freq[word] += 1
    return freq

def process_chunk(filepath, start, end):
    with open(filepath, 'r') as f:
        f.seek(start)
        chunk = f.read(end - start)
    word_freq = word_count(chunk)
    print(json.dumps(word_freq))

def main():
    input_file = '/tmp/file.txt'
    file_size = os.path.getsize(input_file)
    num_processes = mp.cpu_count()

    chunk_size = file_size // num_processes
    processes = []

    for i in range(num_processes):
        start = i * chunk_size
        end = start + chunk_size if i < num_processes - 1 else None
        p = mp.Process(target=process_chunk, args=(input_file, start, end))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()

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 os import json import multiprocessing as mp from collections import defaultdict from functools import partial def word_count(chunk): words = chunk.split() freq = defaultdict(int) for word in words: freq[word] += 1 return freq def process_chunk(filepath, start, end): with open(filepath, 'r') as f: f.seek(start) chunk = f.read(end - start) word_freq = word_count(chunk) print(json.dumps(word_freq)) def main(): input_file = '/tmp/file.txt' file_size = os.path.getsize(input_file) num_processes = mp.cpu_count() chunk_size = file_size // num_processes processes = [] for i in range(num_processes): start = i * chunk_size end = start + chunk_size if i < num_processes - 1 else None p = mp.Process(target=process_chunk, args=(input_file, start, end)) p.start() processes.append(p) for p in processes: p.join() 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 os
import json
import multiprocessing as mp
from collections import defaultdict
from functools import partial

def word_count(chunk):
    words = chunk.split()
    freq = defaultdict(int)
    for word in words:
        freq[word] += 1
    return freq

def process_chunk(filepath, start, end):
    with open(filepath, 'r') as f:
        f.seek(start)
        chunk = f.read(end - start)
    word_freq = word_count(chunk)
    print(json.dumps(word_freq))

def main():
    input_file = '/tmp/file.txt'
    file_size = os.path.getsize(input_file)
    num_processes = mp.cpu_count()

    chunk_size = file_size // num_processes
    processes = []

    for i in range(num_processes):
        start = i * chunk_size
        end = start + chunk_size if i < num_processes - 1 else None
        p = mp.Process(target=process_chunk, args=(input_file, start, end))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()

if __name__ == '__main__':
    main()

And got the output:

{"it": 1, "was": 1, "the": 1, "best": 1, "of": 1, "times,": 1}
{"it": 1, "was": 1, "the": 1, "worst": 1, "of": 1, "times,": 1}
{"it": 1, "was": 1, "the": 1, "age": 1, "of": 1, "wisdom,": 1}
{"it": 1, "was": 1, "the": 1, "age": 1, "of": 1, "foolish": 1}
{"ness,": 1, "it": 1, "was": 1, "the": 1, "epoch": 1, "of": 1}
{"belief,": 1, "it": 1, "was": 1, "the": 1, "epoch": 1, "o": 1}
{"f": 1, "incredulity,": 1, "it": 1, "was": 1, "the": 1}
{"season": 1, "of": 1, "Light,": 1, "it": 1, "was": 1, "th": 1}
{"e": 1, "season": 1, "of": 1, "Darkness,": 1, "it": 1, "w": 1}
{"as": 1, "the": 1, "spring": 1, "of": 1, "hope,": 1, "it": 1}
{"was": 1, "the": 1, "winter": 1, "of": 1, "despair,": 1}
{"we": 1, "had": 1, "everything": 1, "before": 1}
{"us,": 1, "we": 1, "had": 1, "nothing": 1, "before": 1}
{"us,": 1, "we": 1, "were": 1, "all": 1, "going": 1, "dire": 1}
{"ct": 1, "to": 1, "Heaven,": 1, "we": 1, "were": 1, "all": 1}
Process Process-16:
Traceback (most recent call last):
  File "/usr/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.11/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/src/app/main.py", line 18, in process_chunk
    chunk = f.read(end - start)
                   ~~~~^~~~~~~
TypeError: unsupported operand type(s) for -: 'NoneType' and 'int'

PyFunc

Resulting in output: {'it': 10, 'was': 9, 'the': 9, 'best': 1, 'of': 9, 'times,': 2, 'worst': 1, 'age': 2, 'wisdom,': 1, 'foolish': 1, 'ness,': 1, 'epoch': 2, 'belief,': 1, 'o': 1, 'f': 1, 'incredulity,': 1, 'season': 2, 'Light,': 1, 'th': 1, 'e': 1, 'Darkness,': 1, 'w': 1, 'as': 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': 1, 'dire': 1, 'ct': 1, 'to': 1, 'Heaven,': 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}: False

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

Here's a Python program that uses the multiprocessing module to count the frequency of words in a text file in parallel. It divides the file into four parts and processes each part in a separate process. The program prints out four JSON dictionaries, one per line, each containing the word frequencies for a quarter of the file.

import json
import multiprocessing
import os

def count_words(file_part):
    word_count = {}
    with open(file_part, 'r') as f:
        for line in f:
            for word in line.strip().split():
                word = word.lower()
                if word not in word_count:
                    word_count[word] = 0
                word_count[word] += 1
    return word_count

def split_file(file_path, num_parts):
    with open(file_path, 'r') as input_file:
        lines = input_file.readlines()

    split_lines = [lines[i::num_parts] for i in range(num_parts)]
    temp_files = []

    for i, lines in enumerate(split_lines):
        temp_file = f"/tmp/part_{i}.txt"
        with open(temp_file, 'w') as f:
            f.writelines(lines)
        temp_files.append(temp_file)

    return temp_files

def main():
    input_file = "/tmp/file.txt"
    num_parts = 4

    temp_files = split_file(input_file, num_parts)

    with multiprocessing.Pool(processes=num_parts) as pool:
        word_counts = pool.map(count_words, temp_files)

    for word_count in word_counts:
        print(json.dumps(word_count))

    for temp_file in temp_files:
        os.remove(temp_file)

if __name__ == "__main__":
    main()

To run this program, save it as `python_word_count.py` and run it using the command `python python_word_count.py`. Make sure that the input file is located at `/tmp/file.txt`. The program will print out four JSON dictionaries, one per line, each containing the word frequencies for a quarter of the file. You can then merge these dictionaries to get the total word count for the entire file.

Extract Code

I extracted the following code from that output:

import json
import multiprocessing
import os

def count_words(file_part):
    word_count = {}
    with open(file_part, 'r') as f:
        for line in f:
            for word in line.strip().split():
                word = word.lower()
                if word not in word_count:
                    word_count[word] = 0
                word_count[word] += 1
    return word_count

def split_file(file_path, num_parts):
    with open(file_path, 'r') as input_file:
        lines = input_file.readlines()

    split_lines = [lines[i::num_parts] for i in range(num_parts)]
    temp_files = []

    for i, lines in enumerate(split_lines):
        temp_file = f"/tmp/part_{i}.txt"
        with open(temp_file, 'w') as f:
            f.writelines(lines)
        temp_files.append(temp_file)

    return temp_files

def main():
    input_file = "/tmp/file.txt"
    num_parts = 4

    temp_files = split_file(input_file, num_parts)

    with multiprocessing.Pool(processes=num_parts) as pool:
        word_counts = pool.map(count_words, temp_files)

    for word_count in word_counts:
        print(json.dumps(word_count))

    for temp_file in temp_files:
        os.remove(temp_file)

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 import multiprocessing import os def count_words(file_part): word_count = {} with open(file_part, 'r') as f: for line in f: for word in line.strip().split(): word = word.lower() if word not in word_count: word_count[word] = 0 word_count[word] += 1 return word_count def split_file(file_path, num_parts): with open(file_path, 'r') as input_file: lines = input_file.readlines() split_lines = [lines[i::num_parts] for i in range(num_parts)] temp_files = [] for i, lines in enumerate(split_lines): temp_file = f"/tmp/part_{i}.txt" with open(temp_file, 'w') as f: f.writelines(lines) temp_files.append(temp_file) return temp_files def main(): input_file = "/tmp/file.txt" num_parts = 4 temp_files = split_file(input_file, num_parts) with multiprocessing.Pool(processes=num_parts) as pool: word_counts = pool.map(count_words, temp_files) for word_count in word_counts: print(json.dumps(word_count)) for temp_file in temp_files: os.remove(temp_file) 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
import multiprocessing
import os

def count_words(file_part):
    word_count = {}
    with open(file_part, 'r') as f:
        for line in f:
            for word in line.strip().split():
                word = word.lower()
                if word not in word_count:
                    word_count[word] = 0
                word_count[word] += 1
    return word_count

def split_file(file_path, num_parts):
    with open(file_path, 'r') as input_file:
        lines = input_file.readlines()

    split_lines = [lines[i::num_parts] for i in range(num_parts)]
    temp_files = []

    for i, lines in enumerate(split_lines):
        temp_file = f"/tmp/part_{i}.txt"
        with open(temp_file, 'w') as f:
            f.writelines(lines)
        temp_files.append(temp_file)

    return temp_files

def main():
    input_file = "/tmp/file.txt"
    num_parts = 4

    temp_files = split_file(input_file, num_parts)

    with multiprocessing.Pool(processes=num_parts) as pool:
        word_counts = pool.map(count_words, temp_files)

    for word_count in word_counts:
        print(json.dumps(word_count))

    for temp_file in temp_files:
        os.remove(temp_file)

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

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

Sure, here's a Python program using multiprocessing to achieve this. This program assumes that the text file is large enough to be divided into 4 parts.

import os
import json
import multiprocessing
from multiprocessing import Pool
from functools import partial

def count_words(file_path, start, end):
    with open(file_path, 'r') as f:
        f.seek(start)
        lines = f.read(end - start).split()
        word_count = {}
        for word in lines:
            word = word.strip(',.!?;:')
            if word not in word_count:
                word_count[word] = 1
            else:
                word_count[word] += 1
    return word_count

def divide_file(file_path):
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4
    return [(file_path, i, i + chunk_size) for i in range(0, file_size, chunk_size)]

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    chunks = divide_file(file_path)

    with Pool(4) as p:
        results = p.starmap(count_words, chunks)

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

This program first divides the file into 4 chunks based on its size. Then it uses a process pool to count words in each chunk in parallel. The results are printed out as JSON strings. Please note that this is a simple solution and may not work perfectly for very large files or files where word boundaries are not clearly defined by whitespace. Also, the division of the file into chunks doesn't take into account word boundaries, so some words may be split across chunks. Also, this script doesn't handle the case where the file size is not divisible by 4, so you might want to add some error checking or handling for that. Lastly, this script assumes that the file is small enough to fit into memory. If the file is very large, you might need to use a different approach that doesn't require loading the entire file into memory.

Extract Code

I extracted the following code from that output:

import os
import json
import multiprocessing
from multiprocessing import Pool
from functools import partial

def count_words(file_path, start, end):
    with open(file_path, 'r') as f:
        f.seek(start)
        lines = f.read(end - start).split()
        word_count = {}
        for word in lines:
            word = word.strip(',.!?;:')
            if word not in word_count:
                word_count[word] = 1
            else:
                word_count[word] += 1
    return word_count

def divide_file(file_path):
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4
    return [(file_path, i, i + chunk_size) for i in range(0, file_size, chunk_size)]

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    chunks = divide_file(file_path)

    with Pool(4) as p:
        results = p.starmap(count_words, chunks)

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

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 os import json import multiprocessing from multiprocessing import Pool from functools import partial def count_words(file_path, start, end): with open(file_path, 'r') as f: f.seek(start) lines = f.read(end - start).split() word_count = {} for word in lines: word = word.strip(',.!?;:') if word not in word_count: word_count[word] = 1 else: word_count[word] += 1 return word_count def divide_file(file_path): file_size = os.path.getsize(file_path) chunk_size = file_size // 4 return [(file_path, i, i + chunk_size) for i in range(0, file_size, chunk_size)] if __name__ == '__main__': file_path = '/tmp/file.txt' chunks = divide_file(file_path) with Pool(4) as p: results = p.starmap(count_words, chunks) for result in results: print(json.dumps(result)) 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 os
import json
import multiprocessing
from multiprocessing import Pool
from functools import partial

def count_words(file_path, start, end):
    with open(file_path, 'r') as f:
        f.seek(start)
        lines = f.read(end - start).split()
        word_count = {}
        for word in lines:
            word = word.strip(',.!?;:')
            if word not in word_count:
                word_count[word] = 1
            else:
                word_count[word] += 1
    return word_count

def divide_file(file_path):
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4
    return [(file_path, i, i + chunk_size) for i in range(0, file_size, chunk_size)]

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    chunks = divide_file(file_path)

    with Pool(4) as p:
        results = p.starmap(count_words, chunks)

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

And got the output:

{"it": 4, "was": 4, "the": 4, "best": 1, "of": 4, "times": 2, "worst": 1, "age": 2, "wisdom": 1, "foolish": 1}
{"ness": 1, "it": 4, "was": 4, "the": 3, "epoch": 2, "of": 3, "belief": 1, "incredulity": 1, "season": 1, "Light": 1, "th": 1}
{"e": 1, "season": 1, "of": 3, "Darkness": 1, "it": 2, "was": 2, "the": 2, "spring": 1, "hope": 1, "winter": 1, "despair": 1, "we": 1, "had": 1, "everything": 1, "before": 1}
{"us": 2, "we": 3, "had": 1, "nothing": 1, "before": 1, "were": 2, "all": 2, "going": 2, "direct": 2, "to": 1, "Heaven": 1, "the": 1, "other": 1, "way": 1}

PyFunc

Resulting in output: {'it': 10, 'was': 10, 'the': 10, 'best': 1, 'of': 10, 'times': 2, 'worst': 1, 'age': 2, 'wisdom': 1, 'foolish': 1, 'ness': 1, 'epoch': 2, 'belief': 1, 'incredulity': 1, 'season': 2, 'Light': 1, 'th': 1, 'e': 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

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

Here's a self-contained Python program that uses the multiprocessing library to count word frequencies in parallel. It reads the file in chunks and processes each chunk in a separate process. The result is printed as four JSON dictionaries, one per line.

import os
import json
import multiprocessing
from multiprocessing import Pool
from functools import partial

def process_chunk(chunk):
    words = {}
    for line in chunk:
        for word in line.strip().split():
            word = word.lower()
            if word not in words:
                words[word] = 1
            else:
                words[word] += 1
    return words

def main():
    file_path = "/tmp/file.txt"
    num_processes = 4

    if not os.path.isfile(file_path):
        print("File not found.")
        return

    with open(file_path, "r") as f:
        chunk_size = int(os.path.getsize(file_path) / num_processes)
        pool = Pool(num_processes)

        process_chunk_partial = partial(process_chunk, chunk_size=chunk_size)
        results = pool.map(process_chunk_partial, [iter(f)] * num_processes)

        pool.close()
        pool.join()

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

if __name__ == "__main__":
    main()

To run the program, save the code in a file named "python_word_count.py" and execute it using the command:

python /path/to/python_word_count.py

The program will output four JSON dictionaries, one per line.

Extract Code

I extracted the following code from that output:

import os
import json
import multiprocessing
from multiprocessing import Pool
from functools import partial

def process_chunk(chunk):
    words = {}
    for line in chunk:
        for word in line.strip().split():
            word = word.lower()
            if word not in words:
                words[word] = 1
            else:
                words[word] += 1
    return words

def main():
    file_path = "/tmp/file.txt"
    num_processes = 4

    if not os.path.isfile(file_path):
        print("File not found.")
        return

    with open(file_path, "r") as f:
        chunk_size = int(os.path.getsize(file_path) / num_processes)
        pool = Pool(num_processes)

        process_chunk_partial = partial(process_chunk, chunk_size=chunk_size)
        results = pool.map(process_chunk_partial, [iter(f)] * num_processes)

        pool.close()
        pool.join()

        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. import os import json import multiprocessing from multiprocessing import Pool from functools import partial def process_chunk(chunk): words = {} for line in chunk: for word in line.strip().split(): word = word.lower() if word not in words: words[word] = 1 else: words[word] += 1 return words def main(): file_path = "/tmp/file.txt" num_processes = 4 if not os.path.isfile(file_path): print("File not found.") return with open(file_path, "r") as f: chunk_size = int(os.path.getsize(file_path) / num_processes) pool = Pool(num_processes) process_chunk_partial = partial(process_chunk, chunk_size=chunk_size) results = pool.map(process_chunk_partial, [iter(f)] * num_processes) pool.close() pool.join() 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:

import os
import json
import multiprocessing
from multiprocessing import Pool
from functools import partial

def process_chunk(chunk):
    words = {}
    for line in chunk:
        for word in line.strip().split():
            word = word.lower()
            if word not in words:
                words[word] = 1
            else:
                words[word] += 1
    return words

def main():
    file_path = "/tmp/file.txt"
    num_processes = 4

    if not os.path.isfile(file_path):
        print("File not found.")
        return

    with open(file_path, "r") as f:
        chunk_size = int(os.path.getsize(file_path) / num_processes)
        pool = Pool(num_processes)

        process_chunk_partial = partial(process_chunk, chunk_size=chunk_size)
        results = pool.map(process_chunk_partial, [iter(f)] * num_processes)

        pool.close()
        pool.join()

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

if __name__ == "__main__":
    main()

And got the output:

Traceback (most recent call last):
  File "/usr/src/app/main.py", line 41, in <module>
    main()
  File "/usr/src/app/main.py", line 32, in main
    results = pool.map(process_chunk_partial, [iter(f)] * num_processes)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/multiprocessing/pool.py", line 367, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/multiprocessing/pool.py", line 774, in get
    raise self._value
  File "/usr/lib/python3.11/multiprocessing/pool.py", line 540, in _handle_tasks
    put(task)
  File "/usr/lib/python3.11/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
TypeError: cannot pickle '_io.TextIOWrapper' object

PyFunc

Resulting in output: {}

Equal Evaluation

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

Here's a Python program that uses the multiprocessing module to count word frequencies in parallel across 4 cores. It assumes that the input file is large enough to be divided into 4 roughly equal parts.

import json
import multiprocessing
import os
import re

def count_words(file_part):
    word_count = {}
    for line in file_part:
        words = re.findall(r'\b\w+\b', line.lower())
        for word in words:
            if word in word_count:
                word_count[word] += 1
            else:
                word_count[word] = 1
    return word_count

def divide_file(file_path, num_parts):
    with open(file_path, 'r') as file:
        lines = file.readlines()
    part_size = len(lines) // num_parts
    file_parts = [lines[i: i + part_size] for i in range(0, len(lines), part_size)]
    return file_parts

def main():
    file_path = '/tmp/file.txt'
    file_parts = divide_file(file_path, 4)

    with multiprocessing.Pool(4) as pool:
        results = pool.map(count_words, file_parts)

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

if __name__ == "__main__":
    main()

To run the program, save it as `python_word_count.py` and execute it from the command line with `python /path/to/python_word_count.py`. Please note that punctuation is removed from words using the `re` module, so words like "is," will be treated as "is". If you want to keep the punctuation, you can modify the regex pattern `r'\b\w+\b'` accordingly.

Extract Code

I extracted the following code from that output:

import json
import multiprocessing
import os
import re

def count_words(file_part):
    word_count = {}
    for line in file_part:
        words = re.findall(r'\b\w+\b', line.lower())
        for word in words:
            if word in word_count:
                word_count[word] += 1
            else:
                word_count[word] = 1
    return word_count

def divide_file(file_path, num_parts):
    with open(file_path, 'r') as file:
        lines = file.readlines()
    part_size = len(lines) // num_parts
    file_parts = [lines[i: i + part_size] for i in range(0, len(lines), part_size)]
    return file_parts

def main():
    file_path = '/tmp/file.txt'
    file_parts = divide_file(file_path, 4)

    with multiprocessing.Pool(4) as pool:
        results = pool.map(count_words, file_parts)

    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. import json import multiprocessing import os import re def count_words(file_part): word_count = {} for line in file_part: words = re.findall(r'\b\w+\b', line.lower()) for word in words: if word in word_count: word_count[word] += 1 else: word_count[word] = 1 return word_count def divide_file(file_path, num_parts): with open(file_path, 'r') as file: lines = file.readlines() part_size = len(lines) // num_parts file_parts = [lines[i: i + part_size] for i in range(0, len(lines), part_size)] return file_parts def main(): file_path = '/tmp/file.txt' file_parts = divide_file(file_path, 4) with multiprocessing.Pool(4) as pool: results = pool.map(count_words, file_parts) 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:

import json
import multiprocessing
import os
import re

def count_words(file_part):
    word_count = {}
    for line in file_part:
        words = re.findall(r'\b\w+\b', line.lower())
        for word in words:
            if word in word_count:
                word_count[word] += 1
            else:
                word_count[word] = 1
    return word_count

def divide_file(file_path, num_parts):
    with open(file_path, 'r') as file:
        lines = file.readlines()
    part_size = len(lines) // num_parts
    file_parts = [lines[i: i + part_size] for i in range(0, len(lines), part_size)]
    return file_parts

def main():
    file_path = '/tmp/file.txt'
    file_parts = divide_file(file_path, 4)

    with multiprocessing.Pool(4) as pool:
        results = pool.map(count_words, file_parts)

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

if __name__ == "__main__":
    main()

And got the output:

Traceback (most recent call last):
  File "/usr/src/app/main.py", line 36, in <module>
    main()
  File "/usr/src/app/main.py", line 27, in main
    file_parts = divide_file(file_path, 4)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 22, in divide_file
    file_parts = [lines[i: i + part_size] for i in range(0, len(lines), part_size)]
                                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: range() arg 3 must not be zero

PyFunc

Resulting in output: {}