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

**Python Word Count Program** ================================ Below is a self-contained Python program that uses the `concurrent.futures` library to run in parallel on 4 cores, counting the occurrences of each word in a text file located in `/tmp/file.txt`. 

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import json
import multiprocessing
from collections import Counter

def count_words(file_path, start, end):
    """
    Count the occurrences of each word in a portion of a file.

    Args:
        file_path (str): The path to the file.
        start (int): The starting byte offset.
        end (int): The ending byte offset.

    Returns:
        dict: A dictionary with word counts.
    """
    with open(file_path, 'r') as f:
        f.seek(start)
        words = []
        while f.tell() < end:
            line = f.readline()
            if not line:
                break
            words.extend(line.split())
        return dict(Counter(words))

def main():
    file_path = '/tmp/file.txt'
    num_cores = 4

    # Get the file size
    with open(file_path, 'r') as f:
        f.seek(0, 2)
        file_size = f.tell()

    # Calculate the chunk size for each core
    chunk_size = file_size // num_cores

    # Create a pool of worker processes
    with multiprocessing.Pool(processes=num_cores) as pool:
        # Map the count_words function to each chunk
        results = []
        for i in range(num_cores):
            start = i * chunk_size
            end = (i + 1) * chunk_size if i < num_cores - 1 else file_size
            result = pool.apply_async(count_words, (file_path, start, end))
            results.append(result)

        # Get the results and print them as JSON
        for result in results:
            print(json.dumps(result.get()))

if __name__ == '__main__':
    main()
**Usage** -------- To run the program, save it to a file (e.g., `python_word_count.py`), make the file executable with `chmod +x python_word_count.py`, and then run it with `./python_word_count.py`. Note: This program assumes that the file is located in `/tmp/file.txt`. If the file is located elsewhere, you'll need to modify the `file_path` variable accordingly. Also, this program prints the results as separate JSON dictionaries, one per line, as requested. You can merge these dictionaries as needed.

Extract Code

I extracted the following code from that output: 

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import json
import multiprocessing
from collections import Counter

def count_words(file_path, start, end):
    """
    Count the occurrences of each word in a portion of a file.

    Args:
        file_path (str): The path to the file.
        start (int): The starting byte offset.
        end (int): The ending byte offset.

    Returns:
        dict: A dictionary with word counts.
    """
    with open(file_path, 'r') as f:
        f.seek(start)
        words = []
        while f.tell() < end:
            line = f.readline()
            if not line:
                break
            words.extend(line.split())
        return dict(Counter(words))

def main():
    file_path = '/tmp/file.txt'
    num_cores = 4

    # Get the file size
    with open(file_path, 'r') as f:
        f.seek(0, 2)
        file_size = f.tell()

    # Calculate the chunk size for each core
    chunk_size = file_size // num_cores

    # Create a pool of worker processes
    with multiprocessing.Pool(processes=num_cores) as pool:
        # Map the count_words function to each chunk
        results = []
        for i in range(num_cores):
            start = i * chunk_size
            end = (i + 1) * chunk_size if i < num_cores - 1 else file_size
            result = pool.apply_async(count_words, (file_path, start, end))
            results.append(result)

        # Get the results and print them as JSON
        for result in results:
            print(json.dumps(result.get()))

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 from collections import Counter def count_words(file_path, start, end): """ Count the occurrences of each word in a portion of a file. Args: file_path (str): The path to the file. start (int): The starting byte offset. end (int): The ending byte offset. Returns: dict: A dictionary with word counts. """ with open(file_path, 'r') as f: f.seek(start) words = [] while f.tell() < end: line = f.readline() if not line: break words.extend(line.split()) return dict(Counter(words)) def main(): file_path = '/tmp/file.txt' num_cores = 4 # Get the file size with open(file_path, 'r') as f: f.seek(0, 2) file_size = f.tell() # Calculate the chunk size for each core chunk_size = file_size // num_cores # Create a pool of worker processes with multiprocessing.Pool(processes=num_cores) as pool: # Map the count_words function to each chunk results = [] for i in range(num_cores): start = i * chunk_size end = (i + 1) * chunk_size if i < num_cores - 1 else file_size result = pool.apply_async(count_words, (file_path, start, end)) results.append(result) # Get the results and print them as JSON for result in results: print(json.dumps(result.get())) 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
from collections import Counter

def count_words(file_path, start, end):
    """
    Count the occurrences of each word in a portion of a file.

    Args:
        file_path (str): The path to the file.
        start (int): The starting byte offset.
        end (int): The ending byte offset.

    Returns:
        dict: A dictionary with word counts.
    """
    with open(file_path, 'r') as f:
        f.seek(start)
        words = []
        while f.tell() < end:
            line = f.readline()
            if not line:
                break
            words.extend(line.split())
        return dict(Counter(words))

def main():
    file_path = '/tmp/file.txt'
    num_cores = 4

    # Get the file size
    with open(file_path, 'r') as f:
        f.seek(0, 2)
        file_size = f.tell()

    # Calculate the chunk size for each core
    chunk_size = file_size // num_cores

    # Create a pool of worker processes
    with multiprocessing.Pool(processes=num_cores) as pool:
        # Map the count_words function to each chunk
        results = []
        for i in range(num_cores):
            start = i * chunk_size
            end = (i + 1) * chunk_size if i < num_cores - 1 else file_size
            result = pool.apply_async(count_words, (file_path, start, end))
            results.append(result)

        # Get the results and print them as JSON
        for result in results:
            print(json.dumps(result.get()))

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} {"ness,": 1, "it": 6, "was": 6, "the": 7, "epoch": 2, "of": 6, "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} {"e": 1, "season": 1, "of": 3, "Darkness,": 1, "it": 2, "was": 2, "the": 3, "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} {"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': 18, 'was': 18, 'the': 22, 'best': 1, 'of': 19, 'times,': 2, 'worst': 1, 'age': 2, 'wisdom,': 1, 'foolishness,': 1, 'epoch': 4, 'belief,': 2, 'incredulity,': 2, 'season': 5, 'Light,': 2, 'Darkness,': 3, 'spring': 3, 'hope,': 3, 'winter': 3, 'despair,': 3, 'we': 15, 'had': 7, 'everything': 3, 'before': 7, 'us,': 8, 'nothing': 4, 'were': 8, 'all': 8, 'going': 8, 'direct': 8, 'to': 4, 'Heaven,': 4, 'other': 4, 'way': 4, 'ness,': 1, 'e': 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