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

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.

Output

Here's a Python program that reads a text file located at `/tmp/file.txt`, splits the file into four parts, and processes each part in parallel using 4 cores. Each core counts the word frequencies in its respective part and prints a JSON dictionary to stdout. 

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import json
import multiprocessing
import os

def count_words_in_chunk(chunk):
    word_count = {}
    for word in chunk.split():
        word = word.strip()
        if word:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(start, end, file_path, queue):
    with open(file_path, 'r') as file:
        file.seek(start)
        chunk = file.read(end - start)
        word_count = count_words_in_chunk(chunk)
        queue.put(word_count)

def main():
    file_path = '/tmp/file.txt'
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4
    queue = multiprocessing.Queue()
    processes = []

    for i in range(4):
        start = i * chunk_size
        end = (i + 1) * chunk_size if i < 3 else file_size
        process = multiprocessing.Process(target=process_chunk, args=(start, end, file_path, queue))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

    while not queue.empty():
        word_count = queue.get()
        print(json.dumps(word_count))

if __name__ == "__main__":
    main()
### How it works: 1. **File Splitting**: The file is divided into 4 chunks based on its size. Each chunk is processed by a separate process. 2. **Parallel Processing**: The `multiprocessing` module is used to create 4 processes, each handling one chunk of the file. 3. **Word Counting**: Each process counts the frequency of words in its chunk and stores the result in a dictionary. 4. **Output**: Each process puts its word count dictionary into a queue. After all processes finish, the main program retrieves and prints the dictionaries as JSON strings, one per line. ### Running the Program: 1. Save the script as `python_word_count.py`. 2. Make it executable: `chmod +x python_word_count.py`. 3. Run it: `/path/to/python_word_count.py`. ### Example Output: If `/tmp/file.txt` contains the text `"This is is is, an example"`, the output might look like this (order may vary due to parallel processing): 
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{"This": 1, "is": 1}
{"is": 1, "is,": 1}
{"an": 1}
{"example": 1}
You can then merge these JSON dictionaries as needed.

Extract Code

I extracted the following code from that output: 

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import json
import multiprocessing
import os

def count_words_in_chunk(chunk):
    word_count = {}
    for word in chunk.split():
        word = word.strip()
        if word:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(start, end, file_path, queue):
    with open(file_path, 'r') as file:
        file.seek(start)
        chunk = file.read(end - start)
        word_count = count_words_in_chunk(chunk)
        queue.put(word_count)

def main():
    file_path = '/tmp/file.txt'
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4
    queue = multiprocessing.Queue()
    processes = []

    for i in range(4):
        start = i * chunk_size
        end = (i + 1) * chunk_size if i < 3 else file_size
        process = multiprocessing.Process(target=process_chunk, args=(start, end, file_path, queue))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

    while not queue.empty():
        word_count = queue.get()
        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 import multiprocessing import os def count_words_in_chunk(chunk): word_count = {} for word in chunk.split(): word = word.strip() if word: word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(start, end, file_path, queue): with open(file_path, 'r') as file: file.seek(start) chunk = file.read(end - start) word_count = count_words_in_chunk(chunk) queue.put(word_count) def main(): file_path = '/tmp/file.txt' file_size = os.path.getsize(file_path) chunk_size = file_size // 4 queue = multiprocessing.Queue() processes = [] for i in range(4): start = i * chunk_size end = (i + 1) * chunk_size if i < 3 else file_size process = multiprocessing.Process(target=process_chunk, args=(start, end, file_path, queue)) processes.append(process) process.start() for process in processes: process.join() while not queue.empty(): word_count = queue.get() 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: 

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import json
import multiprocessing
import os

def count_words_in_chunk(chunk):
    word_count = {}
    for word in chunk.split():
        word = word.strip()
        if word:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(start, end, file_path, queue):
    with open(file_path, 'r') as file:
        file.seek(start)
        chunk = file.read(end - start)
        word_count = count_words_in_chunk(chunk)
        queue.put(word_count)

def main():
    file_path = '/tmp/file.txt'
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4
    queue = multiprocessing.Queue()
    processes = []

    for i in range(4):
        start = i * chunk_size
        end = (i + 1) * chunk_size if i < 3 else file_size
        process = multiprocessing.Process(target=process_chunk, args=(start, end, file_path, queue))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

    while not queue.empty():
        word_count = queue.get()
        print(json.dumps(word_count))

if __name__ == "__main__":
    main()
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

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