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 self-contained Python program that counts the word frequencies in a text file located at `/tmp/file.txt` using parallel processing on 4 cores: 

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import json
import multiprocessing
import os

def count_words(chunk):
    word_count = {}
    for word in chunk.split():
        word = word.strip('.,!?;:')
        word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    word_count = count_words(chunk)
    print(json.dumps(word_count))

def main():
    file_path = '/tmp/file.txt'
    
    with open(file_path, 'r') as file:
        text = file.read()
        chunk_size = len(text) // 4
        chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
        
        with multiprocessing.Pool(processes=4) as pool:
            pool.map(process_chunk, chunks)

if __name__ == '__main__':
    main()
To run this program, save it to a file named `python_word_count` (without a file extension) and make it executable using `chmod +x python_word_count`. Then, you can run it by calling `/path/to/python_word_count` without any arguments. Here's how the program works: 1. The `count_words` function takes a chunk of text and counts the frequency of each word in that chunk. It returns a dictionary where the keys are the words and the values are their frequencies. 2. The `process_chunk` function takes a chunk of text, calls `count_words` to count the word frequencies, and then prints the resulting dictionary as a JSON string. 3. The `main` function is the entry point of the program. It reads the text file located at `/tmp/file.txt` and splits it into 4 chunks of roughly equal size. 4. It creates a `multiprocessing.Pool` with 4 processes and uses the `map` method to apply the `process_chunk` function to each chunk in parallel. 5. Each process prints its own JSON dictionary of word frequencies to the standard output (stdout). The program will print 4 different JSON dictionaries, one per line, representing the word frequencies in each chunk of the file. You can then merge these dictionaries as needed. Note: Make sure the file `/tmp/file.txt` exists and contains the desired text before running the program.

Extract Code

I extracted the following code from that output: 

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import json
import multiprocessing
import os

def count_words(chunk):
    word_count = {}
    for word in chunk.split():
        word = word.strip('.,!?;:')
        word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    word_count = count_words(chunk)
    print(json.dumps(word_count))

def main():
    file_path = '/tmp/file.txt'
    
    with open(file_path, 'r') as file:
        text = file.read()
        chunk_size = len(text) // 4
        chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
        
        with multiprocessing.Pool(processes=4) as pool:
            pool.map(process_chunk, chunks)

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(chunk): word_count = {} for word in chunk.split(): word = word.strip('.,!?;:') word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(chunk): word_count = count_words(chunk) print(json.dumps(word_count)) def main(): file_path = '/tmp/file.txt' with open(file_path, 'r') as file: text = file.read() chunk_size = len(text) // 4 chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)] with multiprocessing.Pool(processes=4) as pool: pool.map(process_chunk, chunks) 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(chunk):
    word_count = {}
    for word in chunk.split():
        word = word.strip('.,!?;:')
        word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    word_count = count_words(chunk)
    print(json.dumps(word_count))

def main():
    file_path = '/tmp/file.txt'
    
    with open(file_path, 'r') as file:
        text = file.read()
        chunk_size = len(text) // 4
        chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
        
        with multiprocessing.Pool(processes=4) as pool:
            pool.map(process_chunk, chunks)

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

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 self-contained Python program that counts the occurrences of each word in a text file located at `/tmp/file.txt` using parallel processing on 4 cores: 

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import json
import multiprocessing
from collections import Counter

def count_words(start, end):
    word_counts = Counter()
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        if start > 0:
            file.readline()  # Move to the next line if not at the start
        while file.tell() < end:
            line = file.readline()
            if not line:
                break
            words = line.strip().split()
            word_counts.update(words)
    print(json.dumps(word_counts))

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

    with open(file_path, 'r') as file:
        file_size = file.seek(0, 2)
        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 file_size
        process = multiprocessing.Process(target=count_words, args=(start, end))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

if __name__ == '__main__':
    main()
Explanation: 1. We define a function `count_words` that takes the start and end positions of a chunk of the file as arguments. It reads the file from the specified start position until the end position, counts the occurrences of each word using a `Counter` object, and prints the word counts as a JSON dictionary. 2. In the `main` function, we specify the file path and the number of processes to use (4 in this case). 3. We open the file and determine its size using `file.seek(0, 2)`. Then, we calculate the chunk size by dividing the file size by the number of processes. 4. We create a list to store the processes and iterate `num_processes` times. For each iteration: - We calculate the start and end positions of the chunk based on the chunk size and the current process index. - We create a new process using `multiprocessing.Process`, specifying the target function as `count_words` and passing the start and end positions as arguments. - We append the process to the list of processes and start it. 5. Finally, we wait for all processes to finish using `process.join()`. To run the program, save it as a Python file (e.g., `python_word_count.py`) and execute it using the command `/path/to/python_word_count` with no arguments. The program will print out 4 different JSON dictionaries, one per line, representing the word counts from each chunk of the file. You can then merge these dictionaries as needed. Note: Make sure the file `/tmp/file.txt` exists and contains the input text before running the program.

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(start, end):
    word_counts = Counter()
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        if start > 0:
            file.readline()  # Move to the next line if not at the start
        while file.tell() < end:
            line = file.readline()
            if not line:
                break
            words = line.strip().split()
            word_counts.update(words)
    print(json.dumps(word_counts))

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

    with open(file_path, 'r') as file:
        file_size = file.seek(0, 2)
        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 file_size
        process = multiprocessing.Process(target=count_words, args=(start, end))
        processes.append(process)
        process.start()

    for process in processes:
        process.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 json import multiprocessing from collections import Counter def count_words(start, end): word_counts = Counter() with open('/tmp/file.txt', 'r') as file: file.seek(start) if start > 0: file.readline() # Move to the next line if not at the start while file.tell() < end: line = file.readline() if not line: break words = line.strip().split() word_counts.update(words) print(json.dumps(word_counts)) def main(): file_path = '/tmp/file.txt' num_processes = 4 with open(file_path, 'r') as file: file_size = file.seek(0, 2) 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 file_size process = multiprocessing.Process(target=count_words, args=(start, end)) processes.append(process) process.start() for process in processes: process.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: 

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import json
import multiprocessing
from collections import Counter

def count_words(start, end):
    word_counts = Counter()
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        if start > 0:
            file.readline()  # Move to the next line if not at the start
        while file.tell() < end:
            line = file.readline()
            if not line:
                break
            words = line.strip().split()
            word_counts.update(words)
    print(json.dumps(word_counts))

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

    with open(file_path, 'r') as file:
        file_size = file.seek(0, 2)
        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 file_size
        process = multiprocessing.Process(target=count_words, args=(start, end))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

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

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 self-contained Python program that meets your requirements: 

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import json
import multiprocessing
import os

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

def process_chunk(chunk_number):
    chunk_size = os.path.getsize('/tmp/file.txt') // 4
    with open('/tmp/file.txt', 'r') as file:
        file.seek(chunk_number * chunk_size)
        chunk = file.read(chunk_size)
        if chunk_number == 3:
            chunk += file.read()
    word_count = count_words(chunk)
    print(json.dumps(word_count))

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=4)
    pool.map(process_chunk, range(4))
Here's an explanation of the program: 1. The `count_words` function takes a chunk of text and returns a dictionary containing the word counts for that chunk. 2. The `process_chunk` function is the main function that each process will execute. It takes a `chunk_number` parameter to determine which chunk of the file to process. 3. Inside `process_chunk`: - We calculate the `chunk_size` by dividing the total file size by 4 to ensure each process handles approximately 1/4 of the file. - We open the file `/tmp/file.txt` in read mode. - We seek to the appropriate position in the file based on the `chunk_number` and read the corresponding chunk of text. - If it's the last chunk (chunk_number == 3), we also read the remaining text until the end of the file to ensure we process the entire file. - We call the `count_words` function to count the words in the current chunk. - We print the resulting word count dictionary as a JSON string to stdout. 4. In the `if __name__ == '__main__':` block: - We create a multiprocessing pool with 4 processes. - We use `pool.map` to apply the `process_chunk` function to the range of chunk numbers (0 to 3), effectively distributing the work among the 4 processes. To run the program, save it to a file (e.g., `/path/to/python_word_count`) and make sure it has execute permissions. Then, you can run it without any arguments. The program will process the file `/tmp/file.txt` in parallel using 4 cores and print out 4 different JSON dictionaries, one per line, representing the word counts for each chunk of the file. Note: Make sure the file `/tmp/file.txt` exists before running the program.

Extract Code

I extracted the following code from that output: 

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import json
import multiprocessing
import os

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

def process_chunk(chunk_number):
    chunk_size = os.path.getsize('/tmp/file.txt') // 4
    with open('/tmp/file.txt', 'r') as file:
        file.seek(chunk_number * chunk_size)
        chunk = file.read(chunk_size)
        if chunk_number == 3:
            chunk += file.read()
    word_count = count_words(chunk)
    print(json.dumps(word_count))

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=4)
    pool.map(process_chunk, range(4))

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(chunk): word_count = {} for word in chunk.split(): word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(chunk_number): chunk_size = os.path.getsize('/tmp/file.txt') // 4 with open('/tmp/file.txt', 'r') as file: file.seek(chunk_number * chunk_size) chunk = file.read(chunk_size) if chunk_number == 3: chunk += file.read() word_count = count_words(chunk) print(json.dumps(word_count)) if __name__ == '__main__': pool = multiprocessing.Pool(processes=4) pool.map(process_chunk, range(4)) 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(chunk):
    word_count = {}
    for word in chunk.split():
        word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk_number):
    chunk_size = os.path.getsize('/tmp/file.txt') // 4
    with open('/tmp/file.txt', 'r') as file:
        file.seek(chunk_number * chunk_size)
        chunk = file.read(chunk_size)
        if chunk_number == 3:
            chunk += file.read()
    word_count = count_words(chunk)
    print(json.dumps(word_count))

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=4)
    pool.map(process_chunk, range(4))
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} {"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} {"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}

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, 'us,': 2, 'we': 4, 'had': 2, 'nothing': 1, 'before': 2, 'were': 2, 'all': 2, 'going': 2, 'direct': 2, 'to': 1, 'Heaven,': 1, 'other': 1, 'way': 1, 'e': 1, 'Darkness,': 1, 'spring': 1, 'hope,': 1, 'winter': 1, 'despair,': 1, 'everything': 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

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 self-contained Python program that counts the frequency of each word in a text file located at `/tmp/file.txt` using parallel processing on 4 cores: 

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import json
import multiprocessing
import os

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

def process_chunk(chunk):
    word_count = count_words(chunk)
    print(json.dumps(word_count))

def main():
    file_path = "/tmp/file.txt"
    with open(file_path, "r") as file:
        text = file.read()

    chunk_size = len(text) // 4
    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

    with multiprocessing.Pool(processes=4) as pool:
        pool.map(process_chunk, chunks)

if __name__ == "__main__":
    main()
To run this program, save it to a file named `python_word_count` (without any file extension) and make it executable using the following command: 
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chmod +x /path/to/python_word_count
Then, you can run the program by executing the following command: 
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/path/to/python_word_count
Here's how the program works: 1. The `count_words` function takes a chunk of text and counts the frequency of each word in that chunk. It returns a dictionary where the keys are the words and the values are their respective frequencies. 2. The `process_chunk` function is the worker function that will be executed by each process in the pool. It takes a chunk of text, counts the words using the `count_words` function, and prints the resulting dictionary as a JSON string. 3. In the `main` function: - We open the file at `/tmp/file.txt` and read its contents into the `text` variable. - We calculate the chunk size by dividing the length of the text by 4. - We split the text into 4 chunks using list comprehension. - We create a `multiprocessing.Pool` with 4 processes. - We use `pool.map` to apply the `process_chunk` function to each chunk in parallel. 4. Each process in the pool will execute the `process_chunk` function with its assigned chunk of text. It will count the words in that chunk and print the resulting dictionary as a JSON string to the standard output (stdout). 5. The program will print 4 different JSON dictionaries, one per line, corresponding to the word counts of each chunk. Note: Make sure you have Python installed on your system and the path to the Python executable is correctly set in the shebang line (`#!/usr/bin/env python`) at the beginning of the script. You can collect the output from each process and merge the dictionaries manually to get the final word count for the entire file.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python

import json
import multiprocessing
import os

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

def process_chunk(chunk):
    word_count = count_words(chunk)
    print(json.dumps(word_count))

def main():
    file_path = "/tmp/file.txt"
    with open(file_path, "r") as file:
        text = file.read()

    chunk_size = len(text) // 4
    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

    with multiprocessing.Pool(processes=4) as pool:
        pool.map(process_chunk, chunks)

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 python import json import multiprocessing import os def count_words(chunk): word_count = {} for word in chunk.split(): word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(chunk): word_count = count_words(chunk) print(json.dumps(word_count)) def main(): file_path = "/tmp/file.txt" with open(file_path, "r") as file: text = file.read() chunk_size = len(text) // 4 chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)] with multiprocessing.Pool(processes=4) as pool: pool.map(process_chunk, chunks) 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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#!/usr/bin/env python

import json
import multiprocessing
import os

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

def process_chunk(chunk):
    word_count = count_words(chunk)
    print(json.dumps(word_count))

def main():
    file_path = "/tmp/file.txt"
    with open(file_path, "r") as file:
        text = file.read()

    chunk_size = len(text) // 4
    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

    with multiprocessing.Pool(processes=4) as pool:
        pool.map(process_chunk, chunks)

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

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 self-contained Python program that reads a text file located at `/tmp/file.txt`, counts the occurrences of each word in parallel using 4 cores, and prints the JSON output of the word counts: 

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import json
import multiprocessing
import os

def count_words(text):
    word_counts = {}
    for word in text.split():
        word = word.strip('.,?!')
        if word:
            word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def process_chunk(chunk):
    word_counts = count_words(chunk)
    print(json.dumps(word_counts))

def main():
    file_path = '/tmp/file.txt'
    
    with open(file_path, 'r') as file:
        text = file.read()
    
    num_cores = 4
    chunk_size = len(text) // num_cores
    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
    
    with multiprocessing.Pool(processes=num_cores) as pool:
        pool.map(process_chunk, chunks)

if __name__ == '__main__':
    main()
To run this program, save it to a file named `python_word_count` (without any extension) and make it executable using the following command: 
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chmod +x /path/to/python_word_count
Ensure that you replace `/path/to/` with the actual path where you saved the file. Now you can run the program by executing the following command: 
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/path/to/python_word_count
The program will read the text file located at `/tmp/file.txt`, split it into 4 chunks, and process each chunk in parallel using 4 cores. It will print out 4 different JSON dictionaries, one per line, representing the word counts for each chunk. Note that the program assumes the text file exists at `/tmp/file.txt`. If the file is located elsewhere, modify the `file_path` variable accordingly. You can then merge the JSON dictionaries as needed to obtain the final word counts for the entire file.

Extract Code

I extracted the following code from that output: 

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import json
import multiprocessing
import os

def count_words(text):
    word_counts = {}
    for word in text.split():
        word = word.strip('.,?!')
        if word:
            word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def process_chunk(chunk):
    word_counts = count_words(chunk)
    print(json.dumps(word_counts))

def main():
    file_path = '/tmp/file.txt'
    
    with open(file_path, 'r') as file:
        text = file.read()
    
    num_cores = 4
    chunk_size = len(text) // num_cores
    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
    
    with multiprocessing.Pool(processes=num_cores) as pool:
        pool.map(process_chunk, chunks)

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(text): word_counts = {} for word in text.split(): word = word.strip('.,?!') if word: word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def process_chunk(chunk): word_counts = count_words(chunk) print(json.dumps(word_counts)) def main(): file_path = '/tmp/file.txt' with open(file_path, 'r') as file: text = file.read() num_cores = 4 chunk_size = len(text) // num_cores chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)] with multiprocessing.Pool(processes=num_cores) as pool: pool.map(process_chunk, chunks) 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(text):
    word_counts = {}
    for word in text.split():
        word = word.strip('.,?!')
        if word:
            word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def process_chunk(chunk):
    word_counts = count_words(chunk)
    print(json.dumps(word_counts))

def main():
    file_path = '/tmp/file.txt'
    
    with open(file_path, 'r') as file:
        text = file.read()
    
    num_cores = 4
    chunk_size = len(text) // num_cores
    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
    
    with multiprocessing.Pool(processes=num_cores) as pool:
        pool.map(process_chunk, chunks)

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