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

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#!/usr/bin/env python
import json
import multiprocessing

def count_words(chunk):
  """Counts word frequencies in a given chunk of text."""
  word_counts = {}
  for word in chunk.split():
    if word in word_counts:
      word_counts[word] += 1
    else:
      word_counts[word] = 1
  return word_counts

if __name__ == "__main__":
  file_path = "/tmp/file.txt"
  num_processes = 4

  # Read the file and split into chunks
  with open(file_path, "r") as f:
    file_content = f.read()
  chunk_size = len(file_content) // num_processes
  chunks = [file_content[i:i + chunk_size] for i in range(0, len(file_content), chunk_size)]

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

  # Print out the JSON for each chunk
  for result in results:
    print(json.dumps(result))
**Explanation:** 1. **Import necessary modules:** - `json`: For working with JSON data. - `multiprocessing`: For parallel processing. 2. **`count_words(chunk)` function:** - Takes a chunk of text as input. - Creates an empty dictionary `word_counts` to store word frequencies. - Splits the chunk into words using `chunk.split()`. - Iterates through each word: - If the word is already in `word_counts`, increment its count. - Otherwise, add the word to `word_counts` with a count of 1. - Returns the `word_counts` dictionary. 3. **Main execution block (`if __name__ == "__main__":`)** - Defines the file path (`/tmp/file.txt`) and the number of processes (4). - Reads the entire content of the file into `file_content`. - Calculates `chunk_size` to divide the file into roughly equal parts. - Creates a list `chunks` by slicing `file_content` into chunks. - Creates a process pool using `multiprocessing.Pool(processes=num_processes)`. - Uses `pool.map(count_words, chunks)` to apply the `count_words` function to each chunk in parallel. This returns a list of dictionaries, where each dictionary contains word counts for one chunk. - Iterates through the `results` (the list of dictionaries) and prints each dictionary as a JSON string using `json.dumps(result)`. **How to run:** 1. **Save the code:** Save the code as a Python file (e.g., `python_word_count.py`). 2. **Make it executable:** Use `chmod +x python_word_count.py` to make it executable. 3. **Run the script:** Execute the script using `./python_word_count.py`. This will print four JSON objects to the console, one per line, each representing the word counts from one-fourth of the input file. You can then process these JSON objects further to merge the results as needed.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python
import json
import multiprocessing

def count_words(chunk):
  """Counts word frequencies in a given chunk of text."""
  word_counts = {}
  for word in chunk.split():
    if word in word_counts:
      word_counts[word] += 1
    else:
      word_counts[word] = 1
  return word_counts

if __name__ == "__main__":
  file_path = "/tmp/file.txt"
  num_processes = 4

  # Read the file and split into chunks
  with open(file_path, "r") as f:
    file_content = f.read()
  chunk_size = len(file_content) // num_processes
  chunks = [file_content[i:i + chunk_size] for i in range(0, len(file_content), chunk_size)]

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

  # Print out the JSON for each chunk
  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. #!/usr/bin/env python import json import multiprocessing def count_words(chunk): """Counts word frequencies in a given chunk of text.""" word_counts = {} for word in chunk.split(): if word in word_counts: word_counts[word] += 1 else: word_counts[word] = 1 return word_counts if __name__ == "__main__": file_path = "/tmp/file.txt" num_processes = 4 # Read the file and split into chunks with open(file_path, "r") as f: file_content = f.read() chunk_size = len(file_content) // num_processes chunks = [file_content[i:i + chunk_size] for i in range(0, len(file_content), chunk_size)] # Create a process pool with multiprocessing.Pool(processes=num_processes) as pool: # Map the count_words function to each chunk results = pool.map(count_words, chunks) # Print out the JSON for each chunk 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: 

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#!/usr/bin/env python
import json
import multiprocessing

def count_words(chunk):
  """Counts word frequencies in a given chunk of text."""
  word_counts = {}
  for word in chunk.split():
    if word in word_counts:
      word_counts[word] += 1
    else:
      word_counts[word] = 1
  return word_counts

if __name__ == "__main__":
  file_path = "/tmp/file.txt"
  num_processes = 4

  # Read the file and split into chunks
  with open(file_path, "r") as f:
    file_content = f.read()
  chunk_size = len(file_content) // num_processes
  chunks = [file_content[i:i + chunk_size] for i in range(0, len(file_content), chunk_size)]

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

  # Print out the JSON for each chunk
  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

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

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#!/usr/bin/env python
import json
import multiprocessing

def count_words(text):
    """Counts word occurrences in a given text."""
    word_counts = {}
    for word in text.lower().split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

def process_chunk(chunk):
    """Processes a chunk of the file and prints the word counts as JSON."""
    word_counts = count_words(chunk)
    print(json.dumps(word_counts))

if __name__ == "__main__":
    filename = "/tmp/file.txt"
    num_processes = 4

    # Read the entire file
    with open(filename, "r") as f:
        text = f.read()

    # Calculate chunk size
    chunk_size = len(text) // num_processes

    # Create chunks for parallel processing
    chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]

    # Create a process pool
    with multiprocessing.Pool(processes=num_processes) as pool:
        # Process chunks in parallel
        pool.map(process_chunk, chunks)
**Explanation:** 1. **Import Necessary Modules:** - `json`: For working with JSON data. - `multiprocessing`: For parallel processing. 2. **`count_words(text)` Function:** - Takes a string `text` as input. - Converts the text to lowercase and splits it into words. - Creates a dictionary `word_counts` to store word frequencies. - Iterates through the words, incrementing their counts in the dictionary. - Returns the `word_counts` dictionary. 3. **`process_chunk(chunk)` Function:** - Takes a chunk of text as input. - Calls the `count_words` function to get the word counts for the chunk. - Uses `json.dumps` to convert the word counts dictionary to a JSON string and prints it. 4. **Main Execution Block (`if __name__ == "__main__":`)** - Defines the filename and the number of processes. - Reads the entire content of the file into the `text` variable. - Divides the text into equal-sized chunks based on the number of processes. - Creates a process pool using `multiprocessing.Pool`. - Uses `pool.map` to apply the `process_chunk` function to each chunk in parallel. This will print a separate JSON output for each chunk. **To run this script:** 1. **Save it as a Python file (e.g., `python_word_count.py`).** 2. **Make it executable:** 
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3. **Run the script:** 
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This will print four JSON objects to the console (one per line), each representing the word counts for a quarter of the file.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python
import json
import multiprocessing

def count_words(text):
    """Counts word occurrences in a given text."""
    word_counts = {}
    for word in text.lower().split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

def process_chunk(chunk):
    """Processes a chunk of the file and prints the word counts as JSON."""
    word_counts = count_words(chunk)
    print(json.dumps(word_counts))

if __name__ == "__main__":
    filename = "/tmp/file.txt"
    num_processes = 4

    # Read the entire file
    with open(filename, "r") as f:
        text = f.read()

    # Calculate chunk size
    chunk_size = len(text) // num_processes

    # Create chunks for parallel processing
    chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]

    # Create a process pool
    with multiprocessing.Pool(processes=num_processes) as pool:
        # Process chunks in parallel
        pool.map(process_chunk, chunks)

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 def count_words(text): """Counts word occurrences in a given text.""" word_counts = {} for word in text.lower().split(): if word in word_counts: word_counts[word] += 1 else: word_counts[word] = 1 return word_counts def process_chunk(chunk): """Processes a chunk of the file and prints the word counts as JSON.""" word_counts = count_words(chunk) print(json.dumps(word_counts)) if __name__ == "__main__": filename = "/tmp/file.txt" num_processes = 4 # Read the entire file with open(filename, "r") as f: text = f.read() # Calculate chunk size chunk_size = len(text) // num_processes # Create chunks for parallel processing chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)] # Create a process pool with multiprocessing.Pool(processes=num_processes) as pool: # Process chunks in parallel pool.map(process_chunk, chunks) 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

def count_words(text):
    """Counts word occurrences in a given text."""
    word_counts = {}
    for word in text.lower().split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

def process_chunk(chunk):
    """Processes a chunk of the file and prints the word counts as JSON."""
    word_counts = count_words(chunk)
    print(json.dumps(word_counts))

if __name__ == "__main__":
    filename = "/tmp/file.txt"
    num_processes = 4

    # Read the entire file
    with open(filename, "r") as f:
        text = f.read()

    # Calculate chunk size
    chunk_size = len(text) // num_processes

    # Create chunks for parallel processing
    chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]

    # Create a process pool
    with multiprocessing.Pool(processes=num_processes) as pool:
        # Process chunks in parallel
        pool.map(process_chunk, chunks)
And got the output: {"it": 4, "was": 4, "the": 4, "best": 1, "of": 4, "times,": 2, "worst": 1, "age": 2, "wisdom,": 1, "foolish": 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} {"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}

PyFunc

Resulting in output: {'it': 10, 'was': 10, 'the': 10, 'best': 1, 'of': 10, 'times,': 2, 'worst': 1, 'age': 2, 'wisdom,': 1, 'foolish': 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, '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, '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

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#!/usr/bin/env python3

import json
import multiprocessing
import os

def count_words(file_path, start, end):
    """Counts word frequencies in a portion of a file.

    Args:
        file_path: Path to the text file.
        start: Starting byte position.
        end: Ending byte position.

    Returns:
        A dictionary containing word counts.
    """
    word_counts = {}
    with open(file_path, 'r') as file:
        file.seek(start)
        content = file.read(end - start)

    for word in content.split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4

    # Create a pool of 4 worker processes
    with multiprocessing.Pool(processes=4) as pool:
        results = [pool.apply_async(count_words, 
                                   (file_path, i * chunk_size, (i + 1) * chunk_size))
                   for i in range(4)]

        # Print each JSON result on a separate line
        for result in results:
            print(json.dumps(result.get()))
**Explanation:** 1. **Import Necessary Modules:** - `json`: For encoding dictionaries as JSON. - `multiprocessing`: For parallel processing. - `os`: For getting the file size. 2. **`count_words` Function:** - Takes the file path and byte range (start, end) as input. - Reads the specified portion of the file. - Splits the content into words. - Counts word frequencies using a dictionary. - Returns the word count dictionary. 3. **Main Execution Block (`if __name__ == '__main__':`)** - Gets the file path from the command line argument. - Determines the file size. - Calculates the chunk size for each process. - **Parallel Processing:** - Creates a `multiprocessing.Pool` with 4 worker processes. - Uses `pool.apply_async` to run `count_words` asynchronously for each chunk of the file. - **Printing Results:** - Iterates through the results obtained from each process. - Uses `json.dumps` to convert each dictionary to JSON format. - Prints each JSON output on a separate line. **To run the script:** 1. Save the code as a Python file (e.g., `python_word_count.py`). 2. Make it executable: `chmod +x python_word_count.py` 3. Create the input file `/tmp/file.txt` with your text. 4. Run the script: `./python_word_count.py` This will print four JSON objects, each representing the word counts from a different part of the file. You can then process these JSON objects further to merge the results and get the overall word counts.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3

import json
import multiprocessing
import os

def count_words(file_path, start, end):
    """Counts word frequencies in a portion of a file.

    Args:
        file_path: Path to the text file.
        start: Starting byte position.
        end: Ending byte position.

    Returns:
        A dictionary containing word counts.
    """
    word_counts = {}
    with open(file_path, 'r') as file:
        file.seek(start)
        content = file.read(end - start)

    for word in content.split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4

    # Create a pool of 4 worker processes
    with multiprocessing.Pool(processes=4) as pool:
        results = [pool.apply_async(count_words, 
                                   (file_path, i * chunk_size, (i + 1) * chunk_size))
                   for i in range(4)]

        # Print each JSON result on a separate line
        for result in results:
            print(json.dumps(result.get()))

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. #!/usr/bin/env python3 import json import multiprocessing import os def count_words(file_path, start, end): """Counts word frequencies in a portion of a file. Args: file_path: Path to the text file. start: Starting byte position. end: Ending byte position. Returns: A dictionary containing word counts. """ word_counts = {} with open(file_path, 'r') as file: file.seek(start) content = file.read(end - start) for word in content.split(): if word in word_counts: word_counts[word] += 1 else: word_counts[word] = 1 return word_counts if __name__ == '__main__': file_path = '/tmp/file.txt' file_size = os.path.getsize(file_path) chunk_size = file_size // 4 # Create a pool of 4 worker processes with multiprocessing.Pool(processes=4) as pool: results = [pool.apply_async(count_words, (file_path, i * chunk_size, (i + 1) * chunk_size)) for i in range(4)] # Print each JSON result on a separate line for result in results: print(json.dumps(result.get())) 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 python3

import json
import multiprocessing
import os

def count_words(file_path, start, end):
    """Counts word frequencies in a portion of a file.

    Args:
        file_path: Path to the text file.
        start: Starting byte position.
        end: Ending byte position.

    Returns:
        A dictionary containing word counts.
    """
    word_counts = {}
    with open(file_path, 'r') as file:
        file.seek(start)
        content = file.read(end - start)

    for word in content.split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    file_size = os.path.getsize(file_path)
    chunk_size = file_size // 4

    # Create a pool of 4 worker processes
    with multiprocessing.Pool(processes=4) as pool:
        results = [pool.apply_async(count_words, 
                                   (file_path, i * chunk_size, (i + 1) * chunk_size))
                   for i in range(4)]

        # Print each JSON result on a separate line
        for result in results:
            print(json.dumps(result.get()))
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

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import json
import multiprocessing

def count_words(lines):
  """Counts the occurrences of each word in the given lines.

  Args:
    lines: A list of strings, where each string represents a line of text.

  Returns:
    A dictionary where keys are words and values are their counts.
  """
  word_counts = {}
  for line in lines:
    for word in line.split():
      if word in word_counts:
        word_counts[word] += 1
      else:
        word_counts[word] = 1
  return word_counts

def process_chunk(chunk_lines, output_queue):
  """Processes a chunk of lines from the file.

  This function counts the words in the given chunk and puts the result
  in the output queue.

  Args:
    chunk_lines: A list of strings representing a chunk of the file.
    output_queue: A multiprocessing.Queue to store the results.
  """
  chunk_word_counts = count_words(chunk_lines)
  output_queue.put(chunk_word_counts)

if __name__ == '__main__':
  filename = '/tmp/file.txt'
  num_processes = 4

  # Read the file and split into chunks
  with open(filename, 'r') as f:
    lines = f.readlines()
  chunk_size = len(lines) // num_processes
  chunks = [lines[i:i + chunk_size] for i in range(0, len(lines), chunk_size)]

  # Create a queue to store the results from each process
  output_queue = multiprocessing.Queue()

  # Create and start the processes
  processes = []
  for i in range(num_processes):
    process = multiprocessing.Process(target=process_chunk, args=(chunks[i], output_queue))
    processes.append(process)
    process.start()

  # Get the results from the queue and print them as JSON
  for _ in range(num_processes):
    chunk_word_counts = output_queue.get()
    print(json.dumps(chunk_word_counts))

  # Wait for all processes to complete
  for process in processes:
    process.join()
**Explanation:** 1. **Import Necessary Modules:** - `json`: For working with JSON data. - `multiprocessing`: For parallel processing. 2. **`count_words` Function:** - Takes a list of lines as input. - Splits each line into words and counts their occurrences. - Returns a dictionary with word counts. 3. **`process_chunk` Function:** - Takes a chunk of lines and the output queue as input. - Calls `count_words` to get word counts for the chunk. - Puts the resulting word count dictionary into the output queue. 4. **Main Execution Block (`if __name__ == '__main__':`)** - Defines the filename and the number of processes. - Reads the file and divides it into chunks. - Creates a multiprocessing queue to collect results. - Creates and starts processes, each running `process_chunk` on a different chunk of the file. - Retrieves results from the output queue and prints each chunk's word counts as a JSON object on a separate line. - Waits for all processes to finish. **To run this code:** 1. **Save it as a Python file:** For example, `python_word_count.py`. 2. **Make it executable:** `chmod +x python_word_count.py` 3. **Create the input file:** Make sure you have a file at `/tmp/file.txt` with your sample text. 4. **Run the script:** `./python_word_count.py` This will print four JSON objects (one per core) to the console, each representing the word counts for its respective chunk of the file.

Extract Code

I extracted the following code from that output: 

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import json
import multiprocessing

def count_words(lines):
  """Counts the occurrences of each word in the given lines.

  Args:
    lines: A list of strings, where each string represents a line of text.

  Returns:
    A dictionary where keys are words and values are their counts.
  """
  word_counts = {}
  for line in lines:
    for word in line.split():
      if word in word_counts:
        word_counts[word] += 1
      else:
        word_counts[word] = 1
  return word_counts

def process_chunk(chunk_lines, output_queue):
  """Processes a chunk of lines from the file.

  This function counts the words in the given chunk and puts the result
  in the output queue.

  Args:
    chunk_lines: A list of strings representing a chunk of the file.
    output_queue: A multiprocessing.Queue to store the results.
  """
  chunk_word_counts = count_words(chunk_lines)
  output_queue.put(chunk_word_counts)

if __name__ == '__main__':
  filename = '/tmp/file.txt'
  num_processes = 4

  # Read the file and split into chunks
  with open(filename, 'r') as f:
    lines = f.readlines()
  chunk_size = len(lines) // num_processes
  chunks = [lines[i:i + chunk_size] for i in range(0, len(lines), chunk_size)]

  # Create a queue to store the results from each process
  output_queue = multiprocessing.Queue()

  # Create and start the processes
  processes = []
  for i in range(num_processes):
    process = multiprocessing.Process(target=process_chunk, args=(chunks[i], output_queue))
    processes.append(process)
    process.start()

  # Get the results from the queue and print them as JSON
  for _ in range(num_processes):
    chunk_word_counts = output_queue.get()
    print(json.dumps(chunk_word_counts))

  # Wait for all processes to complete
  for process in processes:
    process.join()

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 def count_words(lines): """Counts the occurrences of each word in the given lines. Args: lines: A list of strings, where each string represents a line of text. Returns: A dictionary where keys are words and values are their counts. """ word_counts = {} for line in lines: for word in line.split(): if word in word_counts: word_counts[word] += 1 else: word_counts[word] = 1 return word_counts def process_chunk(chunk_lines, output_queue): """Processes a chunk of lines from the file. This function counts the words in the given chunk and puts the result in the output queue. Args: chunk_lines: A list of strings representing a chunk of the file. output_queue: A multiprocessing.Queue to store the results. """ chunk_word_counts = count_words(chunk_lines) output_queue.put(chunk_word_counts) if __name__ == '__main__': filename = '/tmp/file.txt' num_processes = 4 # Read the file and split into chunks with open(filename, 'r') as f: lines = f.readlines() chunk_size = len(lines) // num_processes chunks = [lines[i:i + chunk_size] for i in range(0, len(lines), chunk_size)] # Create a queue to store the results from each process output_queue = multiprocessing.Queue() # Create and start the processes processes = [] for i in range(num_processes): process = multiprocessing.Process(target=process_chunk, args=(chunks[i], output_queue)) processes.append(process) process.start() # Get the results from the queue and print them as JSON for _ in range(num_processes): chunk_word_counts = output_queue.get() print(json.dumps(chunk_word_counts)) # Wait for all processes to complete for process in processes: process.join() 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

def count_words(lines):
  """Counts the occurrences of each word in the given lines.

  Args:
    lines: A list of strings, where each string represents a line of text.

  Returns:
    A dictionary where keys are words and values are their counts.
  """
  word_counts = {}
  for line in lines:
    for word in line.split():
      if word in word_counts:
        word_counts[word] += 1
      else:
        word_counts[word] = 1
  return word_counts

def process_chunk(chunk_lines, output_queue):
  """Processes a chunk of lines from the file.

  This function counts the words in the given chunk and puts the result
  in the output queue.

  Args:
    chunk_lines: A list of strings representing a chunk of the file.
    output_queue: A multiprocessing.Queue to store the results.
  """
  chunk_word_counts = count_words(chunk_lines)
  output_queue.put(chunk_word_counts)

if __name__ == '__main__':
  filename = '/tmp/file.txt'
  num_processes = 4

  # Read the file and split into chunks
  with open(filename, 'r') as f:
    lines = f.readlines()
  chunk_size = len(lines) // num_processes
  chunks = [lines[i:i + chunk_size] for i in range(0, len(lines), chunk_size)]

  # Create a queue to store the results from each process
  output_queue = multiprocessing.Queue()

  # Create and start the processes
  processes = []
  for i in range(num_processes):
    process = multiprocessing.Process(target=process_chunk, args=(chunks[i], output_queue))
    processes.append(process)
    process.start()

  # Get the results from the queue and print them as JSON
  for _ in range(num_processes):
    chunk_word_counts = output_queue.get()
    print(json.dumps(chunk_word_counts))

  # Wait for all processes to complete
  for process in processes:
    process.join()
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 44, in <module> chunks = [lines[i:i + chunk_size] for i in range(0, len(lines), chunk_size)] ValueError: range() arg 3 must not be zero

PyFunc

Resulting in output: {}

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

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import json
import multiprocessing

def count_words(file_path, start, end):
    """Counts word occurrences in a specified portion of a file.

    Args:
        file_path (str): The path to the text file.
        start (int): The starting byte position for processing.
        end (int): The ending byte position for processing.

    Returns:
        dict: A dictionary where keys are words and values are their counts.
    """

    word_counts = {}
    with open(file_path, 'r') as f:
        f.seek(start)
        text = f.read(end - start)

    for word in text.split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    
    # Get file size to divide for parallel processing
    with open(file_path, 'r') as f:
        file_size = f.seek(0, 2)

    chunk_size = file_size // 4
    chunks = [(i * chunk_size, (i + 1) * chunk_size) for i in range(4)]

    # Use a process pool for parallel execution
    with multiprocessing.Pool(processes=4) as pool:
        results = pool.starmap(count_words, [(file_path, start, end) for start, end in chunks])

    # Output each JSON dictionary on a separate line
    for result in results:
        print(json.dumps(result))
**Explanation:** 1. **Import Necessary Modules:** - `json`: For working with JSON data. - `multiprocessing`: For parallel processing. 2. **`count_words` Function:** - Takes the file path, start position, and end position as arguments. - Reads the specified portion of the file. - Splits the text into words and counts their occurrences using a dictionary. - Returns the word count dictionary. 3. **Main Execution Block (`if __name__ == '__main__':`)** - Defines the file path. - Calculates the file size. - Divides the file into four equal chunks. - Creates a process pool with four processes. - Uses `pool.starmap` to run the `count_words` function in parallel for each chunk. - Iterates through the results (word count dictionaries from each process) and prints each dictionary as a JSON string on a separate line. **To run the script:** 1. Save the code as a Python file (e.g., `python_word_count.py`). 2. Make the script executable: `chmod +x python_word_count.py` 3. Run the script: `./python_word_count.py` This will print four JSON dictionaries, each representing the word counts from a quarter of the input file.

Extract Code

I extracted the following code from that output: 

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import json
import multiprocessing

def count_words(file_path, start, end):
    """Counts word occurrences in a specified portion of a file.

    Args:
        file_path (str): The path to the text file.
        start (int): The starting byte position for processing.
        end (int): The ending byte position for processing.

    Returns:
        dict: A dictionary where keys are words and values are their counts.
    """

    word_counts = {}
    with open(file_path, 'r') as f:
        f.seek(start)
        text = f.read(end - start)

    for word in text.split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    
    # Get file size to divide for parallel processing
    with open(file_path, 'r') as f:
        file_size = f.seek(0, 2)

    chunk_size = file_size // 4
    chunks = [(i * chunk_size, (i + 1) * chunk_size) for i in range(4)]

    # Use a process pool for parallel execution
    with multiprocessing.Pool(processes=4) as pool:
        results = pool.starmap(count_words, [(file_path, start, end) for start, end in chunks])

    # Output each JSON dictionary on a separate line
    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 json import multiprocessing def count_words(file_path, start, end): """Counts word occurrences in a specified portion of a file. Args: file_path (str): The path to the text file. start (int): The starting byte position for processing. end (int): The ending byte position for processing. Returns: dict: A dictionary where keys are words and values are their counts. """ word_counts = {} with open(file_path, 'r') as f: f.seek(start) text = f.read(end - start) for word in text.split(): if word in word_counts: word_counts[word] += 1 else: word_counts[word] = 1 return word_counts if __name__ == '__main__': file_path = '/tmp/file.txt' # Get file size to divide for parallel processing with open(file_path, 'r') as f: file_size = f.seek(0, 2) chunk_size = file_size // 4 chunks = [(i * chunk_size, (i + 1) * chunk_size) for i in range(4)] # Use a process pool for parallel execution with multiprocessing.Pool(processes=4) as pool: results = pool.starmap(count_words, [(file_path, start, end) for start, end in chunks]) # Output each JSON dictionary on a separate line 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: 

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import json
import multiprocessing

def count_words(file_path, start, end):
    """Counts word occurrences in a specified portion of a file.

    Args:
        file_path (str): The path to the text file.
        start (int): The starting byte position for processing.
        end (int): The ending byte position for processing.

    Returns:
        dict: A dictionary where keys are words and values are their counts.
    """

    word_counts = {}
    with open(file_path, 'r') as f:
        f.seek(start)
        text = f.read(end - start)

    for word in text.split():
        if word in word_counts:
            word_counts[word] += 1
        else:
            word_counts[word] = 1
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    
    # Get file size to divide for parallel processing
    with open(file_path, 'r') as f:
        file_size = f.seek(0, 2)

    chunk_size = file_size // 4
    chunks = [(i * chunk_size, (i + 1) * chunk_size) for i in range(4)]

    # Use a process pool for parallel execution
    with multiprocessing.Pool(processes=4) as pool:
        results = pool.starmap(count_words, [(file_path, start, end) for start, end in chunks])

    # Output each JSON dictionary on a separate line
    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

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