Docker Setup

I have setup the docker container to run the model evaluation.

Initial Query

Write a self-contained Python program that prints a JSON output of how often each word appears in a text file located in /tmp/file.txt. So if the file contains "This is is is, an example" then the program should print to stdout. {"This": 1, "is": 2, "is,":1, "an":1, "example":1} The program should be run by calling /path/to/python_word_count with no arguments. Make it run in parallel on 4 cores at the same time, each taking 1/4 of the file. Print out 4 differnt json dicts, one per line. I'll do the merging.

LLM Generation

Query

Write a self-contained Python program that prints a JSON output of how often each word appears in a text file located in /tmp/file.txt. So if the file contains "This is is is, an example" then the program should print to stdout. {"This": 1, "is": 2, "is,":1, "an":1, "example":1} The program should be run by calling /path/to/python_word_count with no arguments. Make it run in parallel on 4 cores at the same time, each taking 1/4 of the file. Print out 4 differnt json dicts, one per line. I'll do the merging.

Output

Here's a Python script that accomplishes this task: 

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#!/usr/bin/env python3

import os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    cleaned_chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count frequencies
    words = cleaned_chunk.split()
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // 4

    # Prepare multiprocessing
    pool = multiprocessing.Pool(processes=4)
    chunks = []

    # Read chunks
    with open(filename, 'r') as f:
        for i in range(4):
            # Move to chunk start
            if i > 0:
                f.seek(i * chunk_size)
                # Discard partial word at chunk boundary
                f.readline()
            
            # Read chunk
            if i < 3:
                chunk = f.read(chunk_size)
            else:
                chunk = f.read()  # Read remainder for last chunk
            
            chunks.append(chunk)

    # Process chunks in parallel
    results = pool.map(count_words_in_chunk, chunks)

    # Print results as JSON
    for result in results:
        print(json.dumps(result))

def main():
    parallel_word_count('/tmp/file.txt')

if __name__ == '__main__':
    main()
Key features: 1. Uses `multiprocessing.Pool` to process file in parallel 2. Splits file into 4 roughly equal chunks 3. Handles chunk boundaries by skipping partial words 4. Converts to lowercase and removes punctuation 5. Prints each chunk's word count as a separate JSON line 6. Can be run directly as a script Performance notes: - Each process gets 1/4 of the file - Uses `multiprocessing` for true parallel processing - Handles files of any size efficiently To use: 1. Save script 2. Make executable: `chmod +x script.py` 3. Run: `./script.py` Example output might look like: 
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{"this": 5, "is": 3, "a": 2, "test": 1}
{"file": 2, "with": 4, "multiple": 1, "words": 3}
{"another": 2, "chunk": 1, "of": 3, "text": 2}
{"final": 1, "chunk": 2, "with": 1, "some": 2}
The script handles various edge cases like: - Different file sizes - Uneven word distributions - Punctuation - Mixed case words

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3

import os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    cleaned_chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count frequencies
    words = cleaned_chunk.split()
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // 4

    # Prepare multiprocessing
    pool = multiprocessing.Pool(processes=4)
    chunks = []

    # Read chunks
    with open(filename, 'r') as f:
        for i in range(4):
            # Move to chunk start
            if i > 0:
                f.seek(i * chunk_size)
                # Discard partial word at chunk boundary
                f.readline()
            
            # Read chunk
            if i < 3:
                chunk = f.read(chunk_size)
            else:
                chunk = f.read()  # Read remainder for last chunk
            
            chunks.append(chunk)

    # Process chunks in parallel
    results = pool.map(count_words_in_chunk, chunks)

    # Print results as JSON
    for result in results:
        print(json.dumps(result))

def main():
    parallel_word_count('/tmp/file.txt')

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 os import json import multiprocessing import re def count_words_in_chunk(chunk): # Remove punctuation and convert to lowercase cleaned_chunk = re.sub(r'[^\w\s]', '', chunk.lower()) # Split into words and count frequencies words = cleaned_chunk.split() word_counts = {} for word in words: word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def parallel_word_count(filename): # Get file size file_size = os.path.getsize(filename) chunk_size = file_size // 4 # Prepare multiprocessing pool = multiprocessing.Pool(processes=4) chunks = [] # Read chunks with open(filename, 'r') as f: for i in range(4): # Move to chunk start if i > 0: f.seek(i * chunk_size) # Discard partial word at chunk boundary f.readline() # Read chunk if i < 3: chunk = f.read(chunk_size) else: chunk = f.read() # Read remainder for last chunk chunks.append(chunk) # Process chunks in parallel results = pool.map(count_words_in_chunk, chunks) # Print results as JSON for result in results: print(json.dumps(result)) def main(): parallel_word_count('/tmp/file.txt') 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 os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    cleaned_chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count frequencies
    words = cleaned_chunk.split()
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // 4

    # Prepare multiprocessing
    pool = multiprocessing.Pool(processes=4)
    chunks = []

    # Read chunks
    with open(filename, 'r') as f:
        for i in range(4):
            # Move to chunk start
            if i > 0:
                f.seek(i * chunk_size)
                # Discard partial word at chunk boundary
                f.readline()
            
            # Read chunk
            if i < 3:
                chunk = f.read(chunk_size)
            else:
                chunk = f.read()  # Read remainder for last chunk
            
            chunks.append(chunk)

    # Process chunks in parallel
    results = pool.map(count_words_in_chunk, chunks)

    # Print results as JSON
    for result in results:
        print(json.dumps(result))

def main():
    parallel_word_count('/tmp/file.txt')

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} {} {} {}

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

Here's a Python script that accomplishes this task: 

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#!/usr/bin/env python3
import json
import os
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def process_file_chunk(start, chunk_size):
    with open('/tmp/file.txt', 'r') as f:
        # Seek to the start of the chunk
        f.seek(start)
        
        # Read the chunk
        chunk = f.read(chunk_size)
        
        # If not at the end, read to the next word boundary
        if start + chunk_size < os.path.getsize('/tmp/file.txt'):
            remainder = f.read(1)
            while remainder and not remainder.isspace():
                chunk += remainder
                remainder = f.read(1)
        
        return count_words_in_chunk(chunk)

def main():
    # Get file size
    file_size = os.path.getsize('/tmp/file.txt')
    
    # Calculate chunk sizes
    num_cores = 4
    chunk_size = file_size // num_cores
    
    # Prepare arguments for each chunk
    chunk_starts = [i * chunk_size for i in range(num_cores)]
    
    # Use multiprocessing to process chunks in parallel
    with multiprocessing.Pool(num_cores) as pool:
        results = pool.starmap(process_file_chunk, 
                               [(start, chunk_size) for start in chunk_starts])
    
    # Print each chunk's word count as a separate JSON
    for result in results:
        print(json.dumps(result))

if __name__ == '__main__':
    main()
Key features: - Uses `multiprocessing.Pool` to process file chunks in parallel - Splits file into 4 roughly equal chunks - Reads each chunk and counts words - Removes punctuation and converts to lowercase - Prints a separate JSON dict for each chunk's word counts - Handles files of any size - Ensures each chunk reads to a word boundary To use: 1. Save this script 2. Make it executable: `chmod +x /path/to/python_word_count` 3. Run it: `/path/to/python_word_count` Example output might look like: 
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{"this": 5, "is": 3, "a": 2}
{"test": 4, "file": 2, "with": 1}
{"some": 3, "more": 2, "words": 1}
{"and": 2, "final": 1, "chunk": 1}
The script does minimal processing to keep it fast and simple. You can merge these JSON dicts using another script or manual aggregation.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import json
import os
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def process_file_chunk(start, chunk_size):
    with open('/tmp/file.txt', 'r') as f:
        # Seek to the start of the chunk
        f.seek(start)
        
        # Read the chunk
        chunk = f.read(chunk_size)
        
        # If not at the end, read to the next word boundary
        if start + chunk_size < os.path.getsize('/tmp/file.txt'):
            remainder = f.read(1)
            while remainder and not remainder.isspace():
                chunk += remainder
                remainder = f.read(1)
        
        return count_words_in_chunk(chunk)

def main():
    # Get file size
    file_size = os.path.getsize('/tmp/file.txt')
    
    # Calculate chunk sizes
    num_cores = 4
    chunk_size = file_size // num_cores
    
    # Prepare arguments for each chunk
    chunk_starts = [i * chunk_size for i in range(num_cores)]
    
    # Use multiprocessing to process chunks in parallel
    with multiprocessing.Pool(num_cores) as pool:
        results = pool.starmap(process_file_chunk, 
                               [(start, chunk_size) for start in chunk_starts])
    
    # Print each chunk's word count as a separate JSON
    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 os import multiprocessing import re def count_words_in_chunk(chunk): # Remove punctuation and convert to lowercase chunk = re.sub(r'[^\w\s]', '', chunk.lower()) # Split into words and count word_counts = {} for word in chunk.split(): word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def process_file_chunk(start, chunk_size): with open('/tmp/file.txt', 'r') as f: # Seek to the start of the chunk f.seek(start) # Read the chunk chunk = f.read(chunk_size) # If not at the end, read to the next word boundary if start + chunk_size < os.path.getsize('/tmp/file.txt'): remainder = f.read(1) while remainder and not remainder.isspace(): chunk += remainder remainder = f.read(1) return count_words_in_chunk(chunk) def main(): # Get file size file_size = os.path.getsize('/tmp/file.txt') # Calculate chunk sizes num_cores = 4 chunk_size = file_size // num_cores # Prepare arguments for each chunk chunk_starts = [i * chunk_size for i in range(num_cores)] # Use multiprocessing to process chunks in parallel with multiprocessing.Pool(num_cores) as pool: results = pool.starmap(process_file_chunk, [(start, chunk_size) for start in chunk_starts]) # Print each chunk's word count as a separate JSON 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 os
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def process_file_chunk(start, chunk_size):
    with open('/tmp/file.txt', 'r') as f:
        # Seek to the start of the chunk
        f.seek(start)
        
        # Read the chunk
        chunk = f.read(chunk_size)
        
        # If not at the end, read to the next word boundary
        if start + chunk_size < os.path.getsize('/tmp/file.txt'):
            remainder = f.read(1)
            while remainder and not remainder.isspace():
                chunk += remainder
                remainder = f.read(1)
        
        return count_words_in_chunk(chunk)

def main():
    # Get file size
    file_size = os.path.getsize('/tmp/file.txt')
    
    # Calculate chunk sizes
    num_cores = 4
    chunk_size = file_size // num_cores
    
    # Prepare arguments for each chunk
    chunk_starts = [i * chunk_size for i in range(num_cores)]
    
    # Use multiprocessing to process chunks in parallel
    with multiprocessing.Pool(num_cores) as pool:
        results = pool.starmap(process_file_chunk, 
                               [(start, chunk_size) for start in chunk_starts])
    
    # Print each chunk's word count as a separate JSON
    for result in results:
        print(json.dumps(result))

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, "foolishness": 1} {"ness": 1, "it": 4, "was": 4, "the": 4, "epoch": 2, "of": 3, "belief": 1, "incredulity": 1, "season": 1, "light": 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": 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': 11, 'best': 1, 'of': 10, 'times': 2, 'worst': 1, 'age': 2, 'wisdom': 1, 'foolishness': 1, 'ness': 1, 'epoch': 2, 'belief': 1, 'incredulity': 1, 'season': 2, 'light': 1, 'e': 1, 'darkness': 1, 'spring': 1, 'hope': 1, 'winter': 1, 'despair': 1, 'we': 4, 'had': 2, 'everything': 1, 'before': 2, 'us': 3, '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 Python script that accomplishes the task: 

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#!/usr/bin/env python3
import json
import re
import multiprocessing
import os

def count_words_in_chunk(chunk):
    # Convert chunk to lowercase and split into words
    words = re.findall(r'\w+', chunk.lower())
    
    # Count word frequencies
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    
    # Prepare chunk ranges
    chunk_size = file_size // 4
    chunks = []
    
    with open(filename, 'r') as f:
        # Create 4 chunks
        for i in range(4):
            # Seek to start of chunk
            f.seek(i * chunk_size)
            
            # Read chunk
            if i < 3:
                # For first 3 chunks, read chunk_size and adjust to word boundary
                chunk = f.read(chunk_size)
                # Seek to next word boundary
                while f.read(1) and not f.read(1).isspace():
                    chunk += f.read(1)
            else:
                # Last chunk reads to end of file
                chunk = f.read()
            
            chunks.append(chunk)
    
    # Process chunks in parallel
    with multiprocessing.Pool(4) as pool:
        results = pool.map(count_words_in_chunk, chunks)
    
    # Print each result as a JSON dictionary
    for result in results:
        print(json.dumps(result))

def main():
    parallel_word_count('/tmp/file.txt')

if __name__ == '__main__':
    main()
Key features: 1. Uses `multiprocessing` to parallelize word counting 2. Splits file into 4 approximately equal chunks 3. Adjusts chunk boundaries to full words 4. Converts words to lowercase 5. Uses regex to extract words 6. Prints JSON dictionaries of word frequencies 7. Can be run directly as a script Usage: - Save as an executable script (e.g., `word_count.py`) - Make executable with `chmod +x word_count.py` - Run with `/path/to/word_count.py` Example input (`/tmp/file.txt`): 
1
This is is is, an example of word counting in parallel processing.
Example output might look like: 
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{"this": 1, "is": 3, "an": 1, "example": 1}
{"of": 1, "word": 1, "counting": 1}
{"in": 1, "parallel": 1, "processing": 1}
{}
Notes: - The script handles files of various sizes - Last chunk might be empty for very small files - Uses non-overlapping chunks for efficiency - Assumes file is readable and exists

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import json
import re
import multiprocessing
import os

def count_words_in_chunk(chunk):
    # Convert chunk to lowercase and split into words
    words = re.findall(r'\w+', chunk.lower())
    
    # Count word frequencies
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    
    # Prepare chunk ranges
    chunk_size = file_size // 4
    chunks = []
    
    with open(filename, 'r') as f:
        # Create 4 chunks
        for i in range(4):
            # Seek to start of chunk
            f.seek(i * chunk_size)
            
            # Read chunk
            if i < 3:
                # For first 3 chunks, read chunk_size and adjust to word boundary
                chunk = f.read(chunk_size)
                # Seek to next word boundary
                while f.read(1) and not f.read(1).isspace():
                    chunk += f.read(1)
            else:
                # Last chunk reads to end of file
                chunk = f.read()
            
            chunks.append(chunk)
    
    # Process chunks in parallel
    with multiprocessing.Pool(4) as pool:
        results = pool.map(count_words_in_chunk, chunks)
    
    # Print each result as a JSON dictionary
    for result in results:
        print(json.dumps(result))

def main():
    parallel_word_count('/tmp/file.txt')

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 re import multiprocessing import os def count_words_in_chunk(chunk): # Convert chunk to lowercase and split into words words = re.findall(r'\w+', chunk.lower()) # Count word frequencies word_counts = {} for word in words: word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def parallel_word_count(filename): # Get file size file_size = os.path.getsize(filename) # Prepare chunk ranges chunk_size = file_size // 4 chunks = [] with open(filename, 'r') as f: # Create 4 chunks for i in range(4): # Seek to start of chunk f.seek(i * chunk_size) # Read chunk if i < 3: # For first 3 chunks, read chunk_size and adjust to word boundary chunk = f.read(chunk_size) # Seek to next word boundary while f.read(1) and not f.read(1).isspace(): chunk += f.read(1) else: # Last chunk reads to end of file chunk = f.read() chunks.append(chunk) # Process chunks in parallel with multiprocessing.Pool(4) as pool: results = pool.map(count_words_in_chunk, chunks) # Print each result as a JSON dictionary for result in results: print(json.dumps(result)) def main(): parallel_word_count('/tmp/file.txt') 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 re
import multiprocessing
import os

def count_words_in_chunk(chunk):
    # Convert chunk to lowercase and split into words
    words = re.findall(r'\w+', chunk.lower())
    
    # Count word frequencies
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    
    # Prepare chunk ranges
    chunk_size = file_size // 4
    chunks = []
    
    with open(filename, 'r') as f:
        # Create 4 chunks
        for i in range(4):
            # Seek to start of chunk
            f.seek(i * chunk_size)
            
            # Read chunk
            if i < 3:
                # For first 3 chunks, read chunk_size and adjust to word boundary
                chunk = f.read(chunk_size)
                # Seek to next word boundary
                while f.read(1) and not f.read(1).isspace():
                    chunk += f.read(1)
            else:
                # Last chunk reads to end of file
                chunk = f.read()
            
            chunks.append(chunk)
    
    # Process chunks in parallel
    with multiprocessing.Pool(4) as pool:
        results = pool.map(count_words_in_chunk, chunks)
    
    # Print each result as a JSON dictionary
    for result in results:
        print(json.dumps(result))

def main():
    parallel_word_count('/tmp/file.txt')

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, "foolishs": 1, "sh": 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, "ea": 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, 'foolishs': 1, 'sh': 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, 'ea': 1, '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 Python script that accomplishes this task: 

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#!/usr/bin/python3

import os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words
    words = chunk.split()
    
    # Count words
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def split_file_into_chunks(filename, num_chunks):
    # Get total file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // num_chunks
    
    chunks = []
    with open(filename, 'r') as f:
        for i in range(num_chunks):
            # Move to start of chunk
            f.seek(i * chunk_size)
            
            # Skip partial word if not first chunk
            if i > 0:
                f.readline()  # Discard partial line
            
            # Read chunk
            if i < num_chunks - 1:
                chunk = f.read(chunk_size)
            else:
                chunk = f.read()  # Read remaining for last chunk
            
            chunks.append(chunk)
    
    return chunks

def main():
    # File to process
    filename = '/tmp/file.txt'
    num_cores = 4
    
    # Split file into chunks
    chunks = split_file_into_chunks(filename, num_cores)
    
    # Use multiprocessing to count words in parallel
    with multiprocessing.Pool(num_cores) as pool:
        results = pool.map(count_words_in_chunk, chunks)
    
    # Print each result as a JSON dict
    for result in results:
        print(json.dumps(result))

if __name__ == '__main__':
    main()
Key features: 1. Uses `multiprocessing.Pool` to parallelize word counting 2. Splits the file into chunks proportional to file size 3. Removes punctuation and converts to lowercase 4. Prints JSON dict for each chunk on a separate line 5. No command-line arguments needed To use: 1. Save script with executable permissions 2. Ensure file exists at `/tmp/file.txt` 3. Run the script directly The script handles: - Parallel processing across 4 cores - Splitting file intelligently - Word counting with frequency - JSON output - Handling files of various sizes Example output might look like: 
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{"this": 2, "is": 3, "a": 1}
{"test": 2, "file": 1, "with": 1}
{"some": 2, "words": 2, "here": 1}
{"and": 1, "there": 1}

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/python3

import os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words
    words = chunk.split()
    
    # Count words
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def split_file_into_chunks(filename, num_chunks):
    # Get total file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // num_chunks
    
    chunks = []
    with open(filename, 'r') as f:
        for i in range(num_chunks):
            # Move to start of chunk
            f.seek(i * chunk_size)
            
            # Skip partial word if not first chunk
            if i > 0:
                f.readline()  # Discard partial line
            
            # Read chunk
            if i < num_chunks - 1:
                chunk = f.read(chunk_size)
            else:
                chunk = f.read()  # Read remaining for last chunk
            
            chunks.append(chunk)
    
    return chunks

def main():
    # File to process
    filename = '/tmp/file.txt'
    num_cores = 4
    
    # Split file into chunks
    chunks = split_file_into_chunks(filename, num_cores)
    
    # Use multiprocessing to count words in parallel
    with multiprocessing.Pool(num_cores) as pool:
        results = pool.map(count_words_in_chunk, chunks)
    
    # Print each result as a JSON dict
    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/python3 import os import json import multiprocessing import re def count_words_in_chunk(chunk): # Remove punctuation and convert to lowercase chunk = re.sub(r'[^\w\s]', '', chunk.lower()) # Split into words words = chunk.split() # Count words word_counts = {} for word in words: word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def split_file_into_chunks(filename, num_chunks): # Get total file size file_size = os.path.getsize(filename) chunk_size = file_size // num_chunks chunks = [] with open(filename, 'r') as f: for i in range(num_chunks): # Move to start of chunk f.seek(i * chunk_size) # Skip partial word if not first chunk if i > 0: f.readline() # Discard partial line # Read chunk if i < num_chunks - 1: chunk = f.read(chunk_size) else: chunk = f.read() # Read remaining for last chunk chunks.append(chunk) return chunks def main(): # File to process filename = '/tmp/file.txt' num_cores = 4 # Split file into chunks chunks = split_file_into_chunks(filename, num_cores) # Use multiprocessing to count words in parallel with multiprocessing.Pool(num_cores) as pool: results = pool.map(count_words_in_chunk, chunks) # Print each result as a JSON dict 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/python3

import os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words
    words = chunk.split()
    
    # Count words
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def split_file_into_chunks(filename, num_chunks):
    # Get total file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // num_chunks
    
    chunks = []
    with open(filename, 'r') as f:
        for i in range(num_chunks):
            # Move to start of chunk
            f.seek(i * chunk_size)
            
            # Skip partial word if not first chunk
            if i > 0:
                f.readline()  # Discard partial line
            
            # Read chunk
            if i < num_chunks - 1:
                chunk = f.read(chunk_size)
            else:
                chunk = f.read()  # Read remaining for last chunk
            
            chunks.append(chunk)
    
    return chunks

def main():
    # File to process
    filename = '/tmp/file.txt'
    num_cores = 4
    
    # Split file into chunks
    chunks = split_file_into_chunks(filename, num_cores)
    
    # Use multiprocessing to count words in parallel
    with multiprocessing.Pool(num_cores) as pool:
        results = pool.map(count_words_in_chunk, chunks)
    
    # Print each result as a JSON dict
    for result in results:
        print(json.dumps(result))

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} {} {} {}

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

Here's a Python script that accomplishes what you described: 

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#!/usr/bin/env python3
import os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // 4

    # Prepare processes
    processes = []
    results = multiprocessing.Manager().list()

    # Spawn 4 processes
    for i in range(4):
        # Calculate start and end positions for each chunk
        start = i * chunk_size
        end = start + chunk_size if i < 3 else file_size

        p = multiprocessing.Process(target=process_chunk, 
                                    args=(filename, start, end, results))
        p.start()
        processes.append(p)

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

    # Convert results to list of dicts
    return list(results)

def process_chunk(filename, start, end, results):
    with open(filename, 'r') as f:
        # Move to start position
        f.seek(start)
        
        # If not first chunk, skip partial word
        if start != 0:
            f.readline()  # Skip partial line
        
        # Read chunk
        chunk = f.read(end - f.tell()) if start != 0 else f.read(end)
        
        # Count words in this chunk
        word_counts = count_words_in_chunk(chunk)
        
        # Add to results
        results.append(word_counts)

def main():
    filename = '/tmp/file.txt'
    word_count_results = parallel_word_count(filename)
    
    # Print each dict on a separate line as JSON
    for result in word_count_results:
        print(json.dumps(result))

if __name__ == '__main__':
    main()
This script does several key things: 1. Uses `multiprocessing` to split the file into 4 chunks 2. Each process reads and processes its chunk independently 3. Normalizes words by converting to lowercase and removing punctuation 4. Prints out 4 separate JSON dictionaries, one per line 5. Can be run directly without arguments A few important design notes: - When splitting the file, it skips a partial line at the start of each chunk (except the first) to avoid splitting words - Uses `multiprocessing.Manager().list()` to safely share results between processes - Handles files of any size - Removes punctuation from words - Converts words to lowercase for consistent counting To use: 1. Save the script 2. Make it executable: `chmod +x /path/to/script` 3. Ensure the input file is at `/tmp/file.txt` 4. Run: `/path/to/script` Example output might look like: 
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{"this": 1, "is": 2, "an": 1, "example": 1}
{"another": 3, "chunk": 1}
{"of": 2, "words": 1}
{"final": 1, "chunk": 1}

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // 4

    # Prepare processes
    processes = []
    results = multiprocessing.Manager().list()

    # Spawn 4 processes
    for i in range(4):
        # Calculate start and end positions for each chunk
        start = i * chunk_size
        end = start + chunk_size if i < 3 else file_size

        p = multiprocessing.Process(target=process_chunk, 
                                    args=(filename, start, end, results))
        p.start()
        processes.append(p)

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

    # Convert results to list of dicts
    return list(results)

def process_chunk(filename, start, end, results):
    with open(filename, 'r') as f:
        # Move to start position
        f.seek(start)
        
        # If not first chunk, skip partial word
        if start != 0:
            f.readline()  # Skip partial line
        
        # Read chunk
        chunk = f.read(end - f.tell()) if start != 0 else f.read(end)
        
        # Count words in this chunk
        word_counts = count_words_in_chunk(chunk)
        
        # Add to results
        results.append(word_counts)

def main():
    filename = '/tmp/file.txt'
    word_count_results = parallel_word_count(filename)
    
    # Print each dict on a separate line as JSON
    for result in word_count_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 os import json import multiprocessing import re def count_words_in_chunk(chunk): # Remove punctuation and convert to lowercase chunk = re.sub(r'[^\w\s]', '', chunk.lower()) # Split into words and count word_counts = {} for word in chunk.split(): word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def parallel_word_count(filename): # Get file size file_size = os.path.getsize(filename) chunk_size = file_size // 4 # Prepare processes processes = [] results = multiprocessing.Manager().list() # Spawn 4 processes for i in range(4): # Calculate start and end positions for each chunk start = i * chunk_size end = start + chunk_size if i < 3 else file_size p = multiprocessing.Process(target=process_chunk, args=(filename, start, end, results)) p.start() processes.append(p) # Wait for all processes to complete for p in processes: p.join() # Convert results to list of dicts return list(results) def process_chunk(filename, start, end, results): with open(filename, 'r') as f: # Move to start position f.seek(start) # If not first chunk, skip partial word if start != 0: f.readline() # Skip partial line # Read chunk chunk = f.read(end - f.tell()) if start != 0 else f.read(end) # Count words in this chunk word_counts = count_words_in_chunk(chunk) # Add to results results.append(word_counts) def main(): filename = '/tmp/file.txt' word_count_results = parallel_word_count(filename) # Print each dict on a separate line as JSON for result in word_count_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 os
import json
import multiprocessing
import re

def count_words_in_chunk(chunk):
    # Remove punctuation and convert to lowercase
    chunk = re.sub(r'[^\w\s]', '', chunk.lower())
    
    # Split into words and count
    word_counts = {}
    for word in chunk.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    
    return word_counts

def parallel_word_count(filename):
    # Get file size
    file_size = os.path.getsize(filename)
    chunk_size = file_size // 4

    # Prepare processes
    processes = []
    results = multiprocessing.Manager().list()

    # Spawn 4 processes
    for i in range(4):
        # Calculate start and end positions for each chunk
        start = i * chunk_size
        end = start + chunk_size if i < 3 else file_size

        p = multiprocessing.Process(target=process_chunk, 
                                    args=(filename, start, end, results))
        p.start()
        processes.append(p)

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

    # Convert results to list of dicts
    return list(results)

def process_chunk(filename, start, end, results):
    with open(filename, 'r') as f:
        # Move to start position
        f.seek(start)
        
        # If not first chunk, skip partial word
        if start != 0:
            f.readline()  # Skip partial line
        
        # Read chunk
        chunk = f.read(end - f.tell()) if start != 0 else f.read(end)
        
        # Count words in this chunk
        word_counts = count_words_in_chunk(chunk)
        
        # Add to results
        results.append(word_counts)

def main():
    filename = '/tmp/file.txt'
    word_count_results = parallel_word_count(filename)
    
    # Print each dict on a separate line as JSON
    for result in word_count_results:
        print(json.dumps(result))

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} {} {} {}

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