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
from collections import Counter

def count_words(chunk):
    """Count word frequencies in a chunk of text."""
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def process_chunk(start, size, file_path):
    """Process a chunk of the file based on byte position."""
    with open(file_path, 'r') as f:
        # Move to the start position
        f.seek(start)
        
        # If not at the beginning of the file, read and discard partial word
        if start > 0:
            f.readline()
        
        # Read the chunk
        chunk = f.read(size)
        
        # If not at the end, discard partial word
        if start + size < file_size:
            last_newline = chunk.rfind('\n')
            if last_newline != -1:
                chunk = chunk[:last_newline]
        
        # Count words and return
        return count_words(chunk)

if __name__ == "__main__":
    file_path = '/tmp/file.txt'
    
    # Get file size
    with open(file_path, 'r') as f:
        f.seek(0, 2)  # Move to the end of file
        file_size = f.tell()
    
    # Calculate chunk size
    chunk_size = file_size // 4
    
    # Create 4 tasks with start and size parameters
    tasks = []
    for i in range(4):
        start = i * chunk_size
        # For the last chunk, make sure we read until the end
        size = chunk_size if i < 3 else (file_size - start)
        tasks.append((start, size, file_path))
    
    # Process chunks in parallel
    with multiprocessing.Pool(4) as pool:
        results = pool.starmap(process_chunk, tasks)
    
    # Print results as JSON
    for result in results:
        print(json.dumps(result))
This program: 1. Divides the file into 4 approximately equal chunks by byte position 2. Processes each chunk on a separate core using multiprocessing 3. Makes sure word boundaries are respected by: - Skipping partial words at the beginning of chunks (except first chunk) - Not including partial words at the end of chunks 4. Counts word frequencies in each chunk 5. Prints 4 separate JSON dictionaries, one per line The program is self-contained and doesn't require any arguments when run.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import json
import multiprocessing
from collections import Counter

def count_words(chunk):
    """Count word frequencies in a chunk of text."""
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def process_chunk(start, size, file_path):
    """Process a chunk of the file based on byte position."""
    with open(file_path, 'r') as f:
        # Move to the start position
        f.seek(start)
        
        # If not at the beginning of the file, read and discard partial word
        if start > 0:
            f.readline()
        
        # Read the chunk
        chunk = f.read(size)
        
        # If not at the end, discard partial word
        if start + size < file_size:
            last_newline = chunk.rfind('\n')
            if last_newline != -1:
                chunk = chunk[:last_newline]
        
        # Count words and return
        return count_words(chunk)

if __name__ == "__main__":
    file_path = '/tmp/file.txt'
    
    # Get file size
    with open(file_path, 'r') as f:
        f.seek(0, 2)  # Move to the end of file
        file_size = f.tell()
    
    # Calculate chunk size
    chunk_size = file_size // 4
    
    # Create 4 tasks with start and size parameters
    tasks = []
    for i in range(4):
        start = i * chunk_size
        # For the last chunk, make sure we read until the end
        size = chunk_size if i < 3 else (file_size - start)
        tasks.append((start, size, file_path))
    
    # Process chunks in parallel
    with multiprocessing.Pool(4) as pool:
        results = pool.starmap(process_chunk, tasks)
    
    # Print results as JSON
    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 python3 import json import multiprocessing from collections import Counter def count_words(chunk): """Count word frequencies in a chunk of text.""" word_counts = {} for word in chunk.split(): word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def process_chunk(start, size, file_path): """Process a chunk of the file based on byte position.""" with open(file_path, 'r') as f: # Move to the start position f.seek(start) # If not at the beginning of the file, read and discard partial word if start > 0: f.readline() # Read the chunk chunk = f.read(size) # If not at the end, discard partial word if start + size < file_size: last_newline = chunk.rfind('\n') if last_newline != -1: chunk = chunk[:last_newline] # Count words and return return count_words(chunk) if __name__ == "__main__": file_path = '/tmp/file.txt' # Get file size with open(file_path, 'r') as f: f.seek(0, 2) # Move to the end of file file_size = f.tell() # Calculate chunk size chunk_size = file_size // 4 # Create 4 tasks with start and size parameters tasks = [] for i in range(4): start = i * chunk_size # For the last chunk, make sure we read until the end size = chunk_size if i < 3 else (file_size - start) tasks.append((start, size, file_path)) # Process chunks in parallel with multiprocessing.Pool(4) as pool: results = pool.starmap(process_chunk, tasks) # Print results as JSON 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 python3
import json
import multiprocessing
from collections import Counter

def count_words(chunk):
    """Count word frequencies in a chunk of text."""
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def process_chunk(start, size, file_path):
    """Process a chunk of the file based on byte position."""
    with open(file_path, 'r') as f:
        # Move to the start position
        f.seek(start)
        
        # If not at the beginning of the file, read and discard partial word
        if start > 0:
            f.readline()
        
        # Read the chunk
        chunk = f.read(size)
        
        # If not at the end, discard partial word
        if start + size < file_size:
            last_newline = chunk.rfind('\n')
            if last_newline != -1:
                chunk = chunk[:last_newline]
        
        # Count words and return
        return count_words(chunk)

if __name__ == "__main__":
    file_path = '/tmp/file.txt'
    
    # Get file size
    with open(file_path, 'r') as f:
        f.seek(0, 2)  # Move to the end of file
        file_size = f.tell()
    
    # Calculate chunk size
    chunk_size = file_size // 4
    
    # Create 4 tasks with start and size parameters
    tasks = []
    for i in range(4):
        start = i * chunk_size
        # For the last chunk, make sure we read until the end
        size = chunk_size if i < 3 else (file_size - start)
        tasks.append((start, size, file_path))
    
    # Process chunks in parallel
    with multiprocessing.Pool(4) as pool:
        results = pool.starmap(process_chunk, tasks)
    
    # Print results as JSON
    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} {} {} {}

PyFunc

Resulting in output: {'it': 4, 'was': 4, 'the': 4, 'best': 1, 'of': 4, 'times,': 2, 'worst': 1, 'age': 2, 'wisdom,': 1, 'foolish': 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 as mp
import os

def count_words_in_chunk(chunk_data):
    """Count the frequency of each word in a chunk of text"""
    word_count = {}
    for line in chunk_data:
        # Split the line into words (handle punctuation attached to words)
        words = line.strip().split()
        for word in words:
            if word in word_count:
                word_count[word] += 1
            else:
                word_count[word] = 1
    return word_count

def process_chunk(file_path, start, end):
    """Process a specific chunk of the file based on byte positions"""
    with open(file_path, 'r') as f:
        # Move to the start position
        f.seek(start)
        
        # If we're not at the beginning of the file, discard partial line
        if start > 0:
            f.readline()
        
        # Read lines until we reach the end of our chunk
        chunk_data = []
        position = f.tell()
        while position < end:
            line = f.readline()
            if not line:  # End of file
                break
            chunk_data.append(line)
            position = f.tell()
    
    return count_words_in_chunk(chunk_data)

def main():
    file_path = "/tmp/file.txt"
    
    # Get file size
    file_size = os.path.getsize(file_path)
    
    # Calculate chunk sizes (roughly equal portions)
    chunk_size = file_size // 4
    
    # Define the start and end positions for each chunk
    chunks = []
    for i in range(4):
        start = i * chunk_size
        end = (i + 1) * chunk_size if i < 3 else file_size
        chunks.append((file_path, start, end))
    
    # Create a pool of workers and process the chunks in parallel
    with mp.Pool(processes=4) as pool:
        results = pool.starmap(process_chunk, chunks)
    
    # Print the results
    for result in results:
        print(json.dumps(result))

if __name__ == "__main__":
    main()
This program: 1. Takes a file path `/tmp/file.txt` 2. Divides the file into 4 roughly equal chunks by byte position 3. Processes each chunk in parallel across 4 cores 4. Handles potential word splitting at chunk boundaries by reading complete lines 5. Prints 4 different JSON dictionaries, one per line, showing word counts for each chunk The program can be run by calling `/path/to/python_word_count` with no arguments.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import json
import multiprocessing as mp
import os

def count_words_in_chunk(chunk_data):
    """Count the frequency of each word in a chunk of text"""
    word_count = {}
    for line in chunk_data:
        # Split the line into words (handle punctuation attached to words)
        words = line.strip().split()
        for word in words:
            if word in word_count:
                word_count[word] += 1
            else:
                word_count[word] = 1
    return word_count

def process_chunk(file_path, start, end):
    """Process a specific chunk of the file based on byte positions"""
    with open(file_path, 'r') as f:
        # Move to the start position
        f.seek(start)
        
        # If we're not at the beginning of the file, discard partial line
        if start > 0:
            f.readline()
        
        # Read lines until we reach the end of our chunk
        chunk_data = []
        position = f.tell()
        while position < end:
            line = f.readline()
            if not line:  # End of file
                break
            chunk_data.append(line)
            position = f.tell()
    
    return count_words_in_chunk(chunk_data)

def main():
    file_path = "/tmp/file.txt"
    
    # Get file size
    file_size = os.path.getsize(file_path)
    
    # Calculate chunk sizes (roughly equal portions)
    chunk_size = file_size // 4
    
    # Define the start and end positions for each chunk
    chunks = []
    for i in range(4):
        start = i * chunk_size
        end = (i + 1) * chunk_size if i < 3 else file_size
        chunks.append((file_path, start, end))
    
    # Create a pool of workers and process the chunks in parallel
    with mp.Pool(processes=4) as pool:
        results = pool.starmap(process_chunk, chunks)
    
    # Print the results
    for result in results:
        print(json.dumps(result))

if __name__ == "__main__":
    main()

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. #!/usr/bin/env python3 import json import multiprocessing as mp import os def count_words_in_chunk(chunk_data): """Count the frequency of each word in a chunk of text""" word_count = {} for line in chunk_data: # Split the line into words (handle punctuation attached to words) words = line.strip().split() for word in words: if word in word_count: word_count[word] += 1 else: word_count[word] = 1 return word_count def process_chunk(file_path, start, end): """Process a specific chunk of the file based on byte positions""" with open(file_path, 'r') as f: # Move to the start position f.seek(start) # If we're not at the beginning of the file, discard partial line if start > 0: f.readline() # Read lines until we reach the end of our chunk chunk_data = [] position = f.tell() while position < end: line = f.readline() if not line: # End of file break chunk_data.append(line) position = f.tell() return count_words_in_chunk(chunk_data) def main(): file_path = "/tmp/file.txt" # Get file size file_size = os.path.getsize(file_path) # Calculate chunk sizes (roughly equal portions) chunk_size = file_size // 4 # Define the start and end positions for each chunk chunks = [] for i in range(4): start = i * chunk_size end = (i + 1) * chunk_size if i < 3 else file_size chunks.append((file_path, start, end)) # Create a pool of workers and process the chunks in parallel with mp.Pool(processes=4) as pool: results = pool.starmap(process_chunk, chunks) # Print the results for result in results: print(json.dumps(result)) if __name__ == "__main__": main() Answer either 'The student passes' or 'The student fails'.

Output

The student passes.

Substring Evaluation

Testing if the previous output contains the string student passes: True

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3
import json
import multiprocessing as mp
import os

def count_words_in_chunk(chunk_data):
    """Count the frequency of each word in a chunk of text"""
    word_count = {}
    for line in chunk_data:
        # Split the line into words (handle punctuation attached to words)
        words = line.strip().split()
        for word in words:
            if word in word_count:
                word_count[word] += 1
            else:
                word_count[word] = 1
    return word_count

def process_chunk(file_path, start, end):
    """Process a specific chunk of the file based on byte positions"""
    with open(file_path, 'r') as f:
        # Move to the start position
        f.seek(start)
        
        # If we're not at the beginning of the file, discard partial line
        if start > 0:
            f.readline()
        
        # Read lines until we reach the end of our chunk
        chunk_data = []
        position = f.tell()
        while position < end:
            line = f.readline()
            if not line:  # End of file
                break
            chunk_data.append(line)
            position = f.tell()
    
    return count_words_in_chunk(chunk_data)

def main():
    file_path = "/tmp/file.txt"
    
    # Get file size
    file_size = os.path.getsize(file_path)
    
    # Calculate chunk sizes (roughly equal portions)
    chunk_size = file_size // 4
    
    # Define the start and end positions for each chunk
    chunks = []
    for i in range(4):
        start = i * chunk_size
        end = (i + 1) * chunk_size if i < 3 else file_size
        chunks.append((file_path, start, end))
    
    # Create a pool of workers and process the chunks in parallel
    with mp.Pool(processes=4) as pool:
        results = pool.starmap(process_chunk, chunks)
    
    # Print the results
    for result in results:
        print(json.dumps(result))

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