Docker Setup

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

Initial Query

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

LLM Generation

Query

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

Output

Here's a self-contained Python program that meets your requirements: 

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#!/usr/bin/env python3

import json
import multiprocessing
import os

def count_words(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    for result in results:
        print(json.dumps(result))
This program does the following: 1. It defines a `count_words` function that counts word occurrences in a specific portion of the file. 2. The `process_chunk` function is a wrapper for `count_words` that takes a tuple of start and end positions. 3. In the main block: - It calculates the file size and divides it into 4 chunks. - It uses a `multiprocessing.Pool` with 4 workers to process these chunks in parallel. - Each worker processes its chunk and returns a dictionary of word counts. - Finally, it prints each result as a JSON string, one per line. To use this program: 1. Save it as a file, for example, `word_count.py`. 2. Make it executable: `chmod +x word_count.py` 3. Place it in a directory in your PATH, or create a symlink to it named `python_word_count` in a directory in your PATH. Then you can run it by simply calling `python_word_count` with no arguments. It will process the file at `/tmp/file.txt` and print four JSON dictionaries, one per line, each representing the word counts for a quarter of the file. Note: This approach may split words that happen to be at the chunk boundaries. For more accurate results, you might want to adjust the chunk boundaries to the nearest newline characters, but this would add complexity to the code.

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(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    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 import os def count_words(start, end): word_count = {} with open('/tmp/file.txt', 'r') as file: file.seek(start) content = file.read(end - start) words = content.split() for word in words: word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(chunk): start, end = chunk return count_words(start, end) if __name__ == '__main__': file_size = os.path.getsize('/tmp/file.txt') chunk_size = file_size // 4 chunks = [ (0, chunk_size), (chunk_size, chunk_size * 2), (chunk_size * 2, chunk_size * 3), (chunk_size * 3, file_size) ] with multiprocessing.Pool(4) as pool: results = pool.map(process_chunk, chunks) 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
import os

def count_words(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    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

Here's a self-contained Python program that meets your requirements: 

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#!/usr/bin/env python3

import json
import multiprocessing
import os

def count_words(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    for result in results:
        print(json.dumps(result))
This program does the following: 1. We define a `count_words` function that takes a start and end position in the file, reads that portion, and counts the words in it. 2. The `process_chunk` function is a wrapper that unpacks the chunk tuple and calls `count_words`. 3. In the main block: - We calculate the size of the file and divide it into 4 chunks. - We create a multiprocessing Pool with 4 workers. - We use `pool.map` to apply `process_chunk` to each chunk in parallel. - Finally, we print each result as a JSON string, one per line. To use this program: 1. Save it as a file, for example, `word_count.py`. 2. Make it executable: `chmod +x word_count.py` 3. Place it in a directory in your PATH, or create a symlink to it named `python_word_count` in a directory in your PATH. Now you can run it by simply calling `python_word_count` with no arguments. It will process the file at `/tmp/file.txt` in parallel on 4 cores and print 4 JSON dictionaries, one per line, each representing the word count for a quarter of the file. Note: This approach may split words that happen to be at the boundaries of chunks. For a more accurate count, you might need to implement a more sophisticated splitting method or merge the results afterwards, handling partial words at chunk boundaries.

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(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    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 import os def count_words(start, end): word_count = {} with open('/tmp/file.txt', 'r') as file: file.seek(start) content = file.read(end - start) words = content.split() for word in words: word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(chunk): start, end = chunk return count_words(start, end) if __name__ == '__main__': file_size = os.path.getsize('/tmp/file.txt') chunk_size = file_size // 4 chunks = [ (0, chunk_size), (chunk_size, chunk_size * 2), (chunk_size * 2, chunk_size * 3), (chunk_size * 3, file_size) ] with multiprocessing.Pool(4) as pool: results = pool.map(process_chunk, chunks) 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
import os

def count_words(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    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

Here's a self-contained Python program that meets your requirements: 

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#!/usr/bin/env python3

import json
import multiprocessing
import os

def count_words(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    for result in results:
        print(json.dumps(result))
This program does the following: 1. We define a `count_words` function that counts the words in a specific portion of the file. 2. The `process_chunk` function is a wrapper that takes a chunk (start and end positions) and calls `count_words`. 3. In the main block: - We calculate the file size and divide it into 4 equal chunks. - We create a multiprocessing Pool with 4 workers. - We use `pool.map` to process each chunk in parallel. - Finally, we print the JSON representation of each chunk's word count. To use this program: 1. Save it as a file, for example, `word_count.py`. 2. Make it executable: `chmod +x word_count.py` 3. Move it to the desired location, e.g., `/path/to/python_word_count` 4. Ensure that the shebang line (`#!/usr/bin/env python3`) at the top of the file correctly points to your Python interpreter. Now you can run the program by simply calling `/path/to/python_word_count` with no arguments. It will process the file `/tmp/file.txt` in parallel using 4 cores and print 4 JSON dictionaries, one per line, representing the word counts for each quarter of the file. Note that this approach may split words that happen to be at the chunk boundaries. For a more accurate count, you might need to implement a more sophisticated chunking method or post-process the results to combine split words.

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(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    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 import os def count_words(start, end): word_count = {} with open('/tmp/file.txt', 'r') as file: file.seek(start) content = file.read(end - start) words = content.split() for word in words: word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(chunk): start, end = chunk return count_words(start, end) if __name__ == '__main__': file_size = os.path.getsize('/tmp/file.txt') chunk_size = file_size // 4 chunks = [ (0, chunk_size), (chunk_size, chunk_size * 2), (chunk_size * 2, chunk_size * 3), (chunk_size * 3, file_size) ] with multiprocessing.Pool(4) as pool: results = pool.map(process_chunk, chunks) 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
import os

def count_words(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    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

Here's a self-contained Python program that meets your requirements: 

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#!/usr/bin/env python3

import json
import multiprocessing
import os

def count_words(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    for result in results:
        print(json.dumps(result))
This program does the following: 1. It defines a `count_words` function that takes a start and end position in the file, reads that portion, and counts the words in it. 2. The `process_chunk` function is a wrapper that unpacks the chunk tuple and calls `count_words`. 3. In the main block: - It calculates the file size and divides it into 4 chunks. - It creates a multiprocessing Pool with 4 workers. - It uses `pool.map` to process each chunk in parallel. - Finally, it prints each result as a JSON string, one per line. To use this program: 1. Save it as a file, for example, `/path/to/python_word_count`. 2. Make it executable: `chmod +x /path/to/python_word_count`. 3. Ensure you have a text file at `/tmp/file.txt`. 4. Run the program: `/path/to/python_word_count`. The program will output 4 JSON dictionaries, one per line, each representing the word count for a quarter of the file. You can then merge these results as needed. Note: This approach might split words that happen to be at the chunk boundaries. For a more accurate count, you might need to implement a more sophisticated chunking method that ensures words are not split.

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(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    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 import os def count_words(start, end): word_count = {} with open('/tmp/file.txt', 'r') as file: file.seek(start) content = file.read(end - start) words = content.split() for word in words: word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(chunk): start, end = chunk return count_words(start, end) if __name__ == '__main__': file_size = os.path.getsize('/tmp/file.txt') chunk_size = file_size // 4 chunks = [ (0, chunk_size), (chunk_size, chunk_size * 2), (chunk_size * 2, chunk_size * 3), (chunk_size * 3, file_size) ] with multiprocessing.Pool(4) as pool: results = pool.map(process_chunk, chunks) 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
import os

def count_words(start, end):
    word_count = {}
    with open('/tmp/file.txt', 'r') as file:
        file.seek(start)
        content = file.read(end - start)
        words = content.split()
        for word in words:
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(chunk):
    start, end = chunk
    return count_words(start, end)

if __name__ == '__main__':
    file_size = os.path.getsize('/tmp/file.txt')
    chunk_size = file_size // 4
    chunks = [
        (0, chunk_size),
        (chunk_size, chunk_size * 2),
        (chunk_size * 2, chunk_size * 3),
        (chunk_size * 3, file_size)
    ]

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

    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

Here's a self-contained Python program that meets your requirements: 

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#!/usr/bin/env python3

import json
import multiprocessing
import os

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

def process_chunk(chunk):
    return json.dumps(count_words(chunk))

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

    with open(file_path, 'r') as file:
        content = file.readlines()

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

    # If there are any remaining lines, add them to the last chunk
    if len(chunks) > num_processes:
        chunks[-2].extend(chunks[-1])
        chunks.pop()

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

    for result in results:
        print(result)

if __name__ == '__main__':
    main()
This program does the following: 1. We define a `count_words` function that takes a chunk of text and returns a dictionary with word counts. 2. The `process_chunk` function wraps `count_words` and returns the result as a JSON string. 3. In the `main` function: - We open the file at '/tmp/file.txt'. - We split the file content into 4 roughly equal chunks. - We use a `multiprocessing.Pool` to process these chunks in parallel on 4 cores. - Each process runs the `process_chunk` function on its chunk of the file. - We print the JSON output from each process, one per line. 4. The `if __name__ == '__main__':` block ensures that the `main()` function is only called when the script is run directly (not imported). To use this script: 1. Save it to a file, for example, `/path/to/python_word_count`. 2. Make it executable with `chmod +x /path/to/python_word_count`. 3. Run it with `/path/to/python_word_count`. The script will read '/tmp/file.txt', process it in parallel using 4 cores, and print 4 JSON dictionaries to stdout, one per line. Each dictionary will represent the word counts for approximately 1/4 of the file. Note: This script assumes that the input file can fit into memory. If you're dealing with very large files, you might need to modify the approach to read the file in chunks instead of all at once.

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

def process_chunk(chunk):
    return json.dumps(count_words(chunk))

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

    with open(file_path, 'r') as file:
        content = file.readlines()

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

    # If there are any remaining lines, add them to the last chunk
    if len(chunks) > num_processes:
        chunks[-2].extend(chunks[-1])
        chunks.pop()

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

    for result in results:
        print(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 import os def count_words(chunk): word_count = {} for line in chunk: words = line.strip().split() for word in words: word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(chunk): return json.dumps(count_words(chunk)) def main(): file_path = '/tmp/file.txt' num_processes = 4 with open(file_path, 'r') as file: content = file.readlines() chunk_size = len(content) // num_processes chunks = [content[i:i + chunk_size] for i in range(0, len(content), chunk_size)] # If there are any remaining lines, add them to the last chunk if len(chunks) > num_processes: chunks[-2].extend(chunks[-1]) chunks.pop() with multiprocessing.Pool(processes=num_processes) as pool: results = pool.map(process_chunk, chunks) for result in results: print(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
import os

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

def process_chunk(chunk):
    return json.dumps(count_words(chunk))

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

    with open(file_path, 'r') as file:
        content = file.readlines()

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

    # If there are any remaining lines, add them to the last chunk
    if len(chunks) > num_processes:
        chunks[-2].extend(chunks[-1])
        chunks.pop()

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

    for result in results:
        print(result)

if __name__ == '__main__':
    main()
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 41, in <module> main() File "/usr/src/app/main.py", line 27, in main chunks = [content[i:i + chunk_size] for i in range(0, len(content), 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