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

Output

Here's a Python program that uses the `concurrent.futures` library to process the text file in parallel on 4 cores. The program reads the file in 4 equal parts and processes each part in a separate thread. The results are then printed as separate JSON dictionaries.

import os
import json
import concurrent.futures

# Set the path to the text file
file_path = "/tmp/file.txt"

# Set the number of cores to use for processing
num_cores = 4

# Open the file and read the contents
with open(file_path, "r") as f:
    text = f.read()

# Split the text into equal parts for each core to process
split_text = [text[i:i+len(text)//num_cores] for i in range(0, len(text), len(text)//num_cores)]

# Define a function to count the words in a given text
def count_words(text):
    word_count = {}
    for word in text.split():
        if word in word_count:
            word_count[word] += 1
        else:
            word_count[word] = 1
    return word_count

# Create an executor that uses all available cores
with concurrent.futures.ProcessPoolExecutor(max_workers=num_cores) as executor:
    # Submit the count_words function for each part of the text
    future_to_words = {executor.submit(count_words, text): text for text in split_text}
    for future in concurrent.futures.as_completed(future_to_words):
        # Print the result as a JSON dictionary
        result = future.result()
        print(json.dumps(result))

Note that the above code will print the word counts in an arbitrary order, as the threads may complete in any order. It also does not include punctuation in the word counts. If you want to include punctuation in the word counts, you can modify the `count_words` function to split on any non-alphanumeric character instead of just spaces. Also note that the above code uses the `ProcessPoolExecutor` class to create a pool of threads. This is done to ensure that the code runs in parallel on 4 cores, even if the system has more or fewer cores available. If you want to use all available cores, you can replace `num_cores` with `os.cpu_count()` in the call to `ProcessPoolExecutor`. Finally, please note that the above code will only work if the text file is small enough to fit in memory. If the file is very large, you may need to read it in smaller chunks or use a different approach to count the words.

Extract Code

I extracted the following code from that output:

import os
import json
import concurrent.futures

# Set the path to the text file
file_path = "/tmp/file.txt"

# Set the number of cores to use for processing
num_cores = 4

# Open the file and read the contents
with open(file_path, "r") as f:
    text = f.read()

# Split the text into equal parts for each core to process
split_text = [text[i:i+len(text)//num_cores] for i in range(0, len(text), len(text)//num_cores)]

# Define a function to count the words in a given text
def count_words(text):
    word_count = {}
    for word in text.split():
        if word in word_count:
            word_count[word] += 1
        else:
            word_count[word] = 1
    return word_count

# Create an executor that uses all available cores
with concurrent.futures.ProcessPoolExecutor(max_workers=num_cores) as executor:
    # Submit the count_words function for each part of the text
    future_to_words = {executor.submit(count_words, text): text for text in split_text}
    for future in concurrent.futures.as_completed(future_to_words):
        # Print the result as a JSON dictionary
        result = future.result()
        print(json.dumps(result))

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. import os import json import concurrent.futures # Set the path to the text file file_path = "/tmp/file.txt" # Set the number of cores to use for processing num_cores = 4 # Open the file and read the contents with open(file_path, "r") as f: text = f.read() # Split the text into equal parts for each core to process split_text = [text[i:i+len(text)//num_cores] for i in range(0, len(text), len(text)//num_cores)] # Define a function to count the words in a given text def count_words(text): word_count = {} for word in text.split(): if word in word_count: word_count[word] += 1 else: word_count[word] = 1 return word_count # Create an executor that uses all available cores with concurrent.futures.ProcessPoolExecutor(max_workers=num_cores) as executor: # Submit the count_words function for each part of the text future_to_words = {executor.submit(count_words, text): text for text in split_text} for future in concurrent.futures.as_completed(future_to_words): # Print the result as a JSON dictionary result = future.result() 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:

import os
import json
import concurrent.futures

# Set the path to the text file
file_path = "/tmp/file.txt"

# Set the number of cores to use for processing
num_cores = 4

# Open the file and read the contents
with open(file_path, "r") as f:
    text = f.read()

# Split the text into equal parts for each core to process
split_text = [text[i:i+len(text)//num_cores] for i in range(0, len(text), len(text)//num_cores)]

# Define a function to count the words in a given text
def count_words(text):
    word_count = {}
    for word in text.split():
        if word in word_count:
            word_count[word] += 1
        else:
            word_count[word] = 1
    return word_count

# Create an executor that uses all available cores
with concurrent.futures.ProcessPoolExecutor(max_workers=num_cores) as executor:
    # Submit the count_words function for each part of the text
    future_to_words = {executor.submit(count_words, text): text for text in split_text}
    for future in concurrent.futures.as_completed(future_to_words):
        # Print the result as a JSON dictionary
        result = future.result()
        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}
{"us,": 2, "we": 3, "had": 1, "nothing": 1, "before": 1, "were": 2, "all": 2, "going": 2, "direct": 2, "to": 1, "Heaven,": 1, "the": 1, "other": 1, "way": 1}
{"e": 1, "season": 1, "of": 3, "Darkness,": 1, "it": 2, "was": 2, "the": 2, "spring": 1, "hope,": 1, "winter": 1, "despair,": 1, "we": 1, "had": 1, "everything": 1, "before": 1}

PyFunc

Resulting in output: {'it': 10, 'was': 10, 'the': 10, 'best': 1, 'of': 10, 'times,': 2, 'worst': 1, 'age': 2, 'wisdom,': 1, 'foolish': 1, 'ness,': 1, 'epoch': 2, 'belief,': 1, 'incredulity,': 1, 'season': 2, 'Light,': 1, 'th': 1, 'us,': 2, 'we': 4, 'had': 2, 'nothing': 1, 'before': 2, 'were': 2, 'all': 2, 'going': 2, 'direct': 2, 'to': 1, 'Heaven,': 1, 'other': 1, 'way': 1, 'e': 1, 'Darkness,': 1, 'spring': 1, 'hope,': 1, 'winter': 1, 'despair,': 1, 'everything': 1}

Equal Evaluation

Testing if the previous output equals the string {'the': 11, 'it': 10, 'was': 10, 'of': 10, 'we': 4, 'times,': 2, 'age': 2, 'epoch': 2, 'season': 2, 'had': 2, 'before': 2, 'us,': 2, 'were': 2, 'all': 2, 'going': 2, 'direct': 2, 'best': 1, 'worst': 1, 'wisdom,': 1, 'foolishness,': 1, 'belief,': 1, 'incredulity,': 1, 'Light,': 1, 'Darkness,': 1, 'spring': 1, 'hope,': 1, 'winter': 1, 'despair,': 1, 'everything': 1, 'nothing': 1, 'to': 1, 'Heaven,': 1, 'other': 1, 'way': 1}: False

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

Here's a possible solution that uses Python's `concurrent.futures` module to run the word counting in parallel, and the `json` module to print the results as JSON.

import os
import json
import re
import concurrent.futures

def word_count(lines):
    words = {}
    for line in lines:
        for word in re.findall(r'\w+', line):
            word = word.lower()
            words[word] = words.get(word, 0) + 1
    return words

def process_chunk(file_path, chunk_size):
    with open(file_path, 'r') as f:
        f.seek(chunk_size * (os.getpid() - 1))
        lines = f.read(chunk_size).splitlines()
    return word_count(lines)

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
        chunks = [os.path.getsize(file_path) // 4] * 4
        results = [executor.submit(process_chunk, file_path, chunk) for chunk in chunks]
        for result in results:
            print(json.dumps(result.result()))

This program reads the file in 4 equal parts, using 4 different processes to count the words in each part. Each process returns a dictionary of word counts, which is then printed as JSON. Note that the current implementation splits the file into equal parts, but this may result in some words being split across 2 parts. If this is a problem, you could modify the program to split the file at word boundaries instead. Also note that the program uses the `os.getpid()` function to determine the current process ID, so that each process reads a different part of the file. If you want to run this program on a system that doesn't support processes (e.g. a single-threaded environment), you can modify the program to use threads instead. Finally, note that the current implementation does not handle the case where the file size is not divisible by 4. If this is a problem, you could add some logic to handle the remaining bytes at the end of the file.

Extract Code

I extracted the following code from that output:

import os
import json
import re
import concurrent.futures

def word_count(lines):
    words = {}
    for line in lines:
        for word in re.findall(r'\w+', line):
            word = word.lower()
            words[word] = words.get(word, 0) + 1
    return words

def process_chunk(file_path, chunk_size):
    with open(file_path, 'r') as f:
        f.seek(chunk_size * (os.getpid() - 1))
        lines = f.read(chunk_size).splitlines()
    return word_count(lines)

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
        chunks = [os.path.getsize(file_path) // 4] * 4
        results = [executor.submit(process_chunk, file_path, chunk) for chunk in chunks]
        for result in results:
            print(json.dumps(result.result()))

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. import os import json import re import concurrent.futures def word_count(lines): words = {} for line in lines: for word in re.findall(r'\w+', line): word = word.lower() words[word] = words.get(word, 0) + 1 return words def process_chunk(file_path, chunk_size): with open(file_path, 'r') as f: f.seek(chunk_size * (os.getpid() - 1)) lines = f.read(chunk_size).splitlines() return word_count(lines) if __name__ == '__main__': file_path = '/tmp/file.txt' with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor: chunks = [os.path.getsize(file_path) // 4] * 4 results = [executor.submit(process_chunk, file_path, chunk) for chunk in chunks] for result in results: print(json.dumps(result.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:

import os
import json
import re
import concurrent.futures

def word_count(lines):
    words = {}
    for line in lines:
        for word in re.findall(r'\w+', line):
            word = word.lower()
            words[word] = words.get(word, 0) + 1
    return words

def process_chunk(file_path, chunk_size):
    with open(file_path, 'r') as f:
        f.seek(chunk_size * (os.getpid() - 1))
        lines = f.read(chunk_size).splitlines()
    return word_count(lines)

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
        chunks = [os.path.getsize(file_path) // 4] * 4
        results = [executor.submit(process_chunk, file_path, chunk) for chunk in chunks]
        for result in results:
            print(json.dumps(result.result()))

And got the output:

{}
{}
{}
{}

PyFunc

Resulting in output: {}

Equal Evaluation

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

Here is a possible solution using the `concurrent.futures` library to process the file in parallel with 4 worker threads. This program reads the file in 4 equal chunks and counts the word frequencies in each chunk separately.

import os
import json
import re
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor

def word_count(lines):
    words = re.findall(r'\w+', ' '.join(lines))
    return dict((word, words.count(word)) for word in set(words))

def process_chunk(file, chunk_size):
    with open(file, 'r') as f:
        lines = [f.readline() for _ in range(chunk_size)]
        result = word_count(lines)
        print(json.dumps(result))

if __name__ == '__main__':
    file = '/tmp/file.txt'
    with open(file, 'r') as f:
        num_chunks = 4
        chunk_size = (sum(1 for line in f) + num_chunks - 1) // num_chunks
        f.seek(0)
        with ThreadPoolExecutor(max_workers=4) as executor:
            for i in range(num_chunks):
                executor.submit(process_chunk, file, chunk_size)

Note that this program prints the JSON dictionaries to stdout without any merging, as per your request. Also note that the regular expression used to extract words from the input file may not cover all edge cases and may need to be adjusted depending on the specific requirements of your use case. Additionally, it's important to note that processing the file in parallel may not provide a significant speedup for small files or files with very large lines, as the overhead of creating and coordinating the threads may outweigh the benefits of parallelism. In such cases, it may be more efficient to process the file serially.

Extract Code

I extracted the following code from that output:

import os
import json
import re
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor

def word_count(lines):
    words = re.findall(r'\w+', ' '.join(lines))
    return dict((word, words.count(word)) for word in set(words))

def process_chunk(file, chunk_size):
    with open(file, 'r') as f:
        lines = [f.readline() for _ in range(chunk_size)]
        result = word_count(lines)
        print(json.dumps(result))

if __name__ == '__main__':
    file = '/tmp/file.txt'
    with open(file, 'r') as f:
        num_chunks = 4
        chunk_size = (sum(1 for line in f) + num_chunks - 1) // num_chunks
        f.seek(0)
        with ThreadPoolExecutor(max_workers=4) as executor:
            for i in range(num_chunks):
                executor.submit(process_chunk, file, chunk_size)

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. import os import json import re from collections import defaultdict from concurrent.futures import ThreadPoolExecutor def word_count(lines): words = re.findall(r'\w+', ' '.join(lines)) return dict((word, words.count(word)) for word in set(words)) def process_chunk(file, chunk_size): with open(file, 'r') as f: lines = [f.readline() for _ in range(chunk_size)] result = word_count(lines) print(json.dumps(result)) if __name__ == '__main__': file = '/tmp/file.txt' with open(file, 'r') as f: num_chunks = 4 chunk_size = (sum(1 for line in f) + num_chunks - 1) // num_chunks f.seek(0) with ThreadPoolExecutor(max_workers=4) as executor: for i in range(num_chunks): executor.submit(process_chunk, file, chunk_size) 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:

import os
import json
import re
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor

def word_count(lines):
    words = re.findall(r'\w+', ' '.join(lines))
    return dict((word, words.count(word)) for word in set(words))

def process_chunk(file, chunk_size):
    with open(file, 'r') as f:
        lines = [f.readline() for _ in range(chunk_size)]
        result = word_count(lines)
        print(json.dumps(result))

if __name__ == '__main__':
    file = '/tmp/file.txt'
    with open(file, 'r') as f:
        num_chunks = 4
        chunk_size = (sum(1 for line in f) + num_chunks - 1) // num_chunks
        f.seek(0)
        with ThreadPoolExecutor(max_workers=4) as executor:
            for i in range(num_chunks):
                executor.submit(process_chunk, file, chunk_size)

And got the output:

{"belief": 1, "epoch": 2, "age": 2, "despair": 1, "were": 2, "spring": 1, "Darkness": 1, "worst": 1, "to": 1, "way": 1, "the": 11, "us": 2, "best": 1, "winter": 1, "Light": 1, "Heaven": 1, "of": 10, "we": 4, "wisdom": 1, "before": 2, "going": 2, "other": 1, "direct": 2, "was": 10, "all": 2, "season": 2, "everything": 1, "had": 2, "times": 2, "it": 10, "incredulity": 1, "foolishness": 1, "hope": 1, "nothing": 1}
{"belief": 1, "epoch": 2, "age": 2, "despair": 1, "were": 2, "spring": 1, "Darkness": 1, "worst": 1, "to": 1, "way": 1, "the": 11, "us": 2, "best": 1, "winter": 1, "Light": 1, "Heaven": 1, "of": 10, "we": 4, "wisdom": 1, "before": 2, "going": 2, "other": 1, "direct": 2, "was": 10, "all": 2, "season": 2, "everything": 1, "had": 2, "times": 2, "it": 10, "incredulity": 1, "foolishness": 1, "hope": 1, "nothing": 1}
{"belief": 1, "epoch": 2, "age": 2, "despair": 1, "were": 2, "spring": 1, "Darkness": 1, "worst": 1, "to": 1, "way": 1, "the": 11, "us": 2, "best": 1, "winter": 1, "Light": 1, "Heaven": 1, "of": 10, "we": 4, "wisdom": 1, "before": 2, "going": 2, "other": 1, "direct": 2, "was": 10, "all": 2, "season": 2, "everything": 1, "had": 2, "times": 2, "it": 10, "incredulity": 1, "foolishness": 1, "hope": 1, "nothing": 1}
{"belief": 1, "epoch": 2, "age": 2, "despair": 1, "were": 2, "spring": 1, "Darkness": 1, "worst": 1, "to": 1, "way": 1, "the": 11, "us": 2, "best": 1, "winter": 1, "Light": 1, "Heaven": 1, "of": 10, "we": 4, "wisdom": 1, "before": 2, "going": 2, "other": 1, "direct": 2, "was": 10, "all": 2, "season": 2, "everything": 1, "had": 2, "times": 2, "it": 10, "incredulity": 1, "foolishness": 1, "hope": 1, "nothing": 1}

PyFunc

Resulting in output: {'belief': 4, 'epoch': 8, 'age': 8, 'despair': 4, 'were': 8, 'spring': 4, 'Darkness': 4, 'worst': 4, 'to': 4, 'way': 4, 'the': 44, 'us': 8, 'best': 4, 'winter': 4, 'Light': 4, 'Heaven': 4, 'of': 40, 'we': 16, 'wisdom': 4, 'before': 8, 'going': 8, 'other': 4, 'direct': 8, 'was': 40, 'all': 8, 'season': 8, 'everything': 4, 'had': 8, 'times': 8, 'it': 40, 'incredulity': 4, 'foolishness': 4, 'hope': 4, 'nothing': 4}

Equal Evaluation

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

Here's a Python program that uses the `concurrent.futures` library to process the file in parallel on 4 cores, and prints out 4 separate JSON dictionaries, one per line:

import json
import os
import re
from concurrent.futures import ThreadPoolExecutor

def count_words(lines):
    word_count = {}
    for line in lines:
        words = re.findall(r'\w+', line)
        for word in words:
            word = word.lower()
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(file_path, chunk_size):
    with open(file_path, 'r') as f:
        lines = f.readlines()
    start = 0
    end = len(lines)
    word_counts = []
    for i in range(4):
        if i == 3:
            end_index = end
        else:
            end_index = start + chunk_size
        word_counts.append(count_words(lines[start:end_index]))
        start = end_index
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    chunk_size = int(os.path.getsize(file_path) / 4)
    with ThreadPoolExecutor(max_workers=4) as executor:
        word_counts = list(executor.map(lambda x: process_chunk(file_path, chunk_size), [0]*4))
        for wc in word_counts:
            print(json.dumps(wc, indent=2))

This program first imports the required libraries, and defines a `count_words` function that takes a list of lines and returns a dictionary of word counts. The `process_chunk` function reads the file, divides it into 4 equal chunks, and then calls the `count_words` function on each chunk. The main part of the program sets the file path and chunk size, creates a `ThreadPoolExecutor` with 4 workers, and then maps the `process_chunk` function to the file path 4 times (since we want to process the file 4 times, each time on a different chunk). The results are stored in a list, and then each dictionary is printed to stdout as JSON. Note that this program assumes that the file is small enough to fit in memory. If the file is very large, you may need to read and process it in smaller chunks instead of reading the entire file into memory all at once. Also this program does not do any error handling, you may want to add some try-except blocks to handle cases where the file does not exist, or where the word count calculation fails for some reason. Also the regex pattern used here is simple and it may not cover all possible edge cases, you can use more complex pattern if you want to handle more cases. Also, the program does not handle the case where the file size is not exactly divisible by 4, you may want to handle that case to ensure that the last chunk does not have fewer lines than the others.

Extract Code

I extracted the following code from that output:

import json
import os
import re
from concurrent.futures import ThreadPoolExecutor

def count_words(lines):
    word_count = {}
    for line in lines:
        words = re.findall(r'\w+', line)
        for word in words:
            word = word.lower()
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(file_path, chunk_size):
    with open(file_path, 'r') as f:
        lines = f.readlines()
    start = 0
    end = len(lines)
    word_counts = []
    for i in range(4):
        if i == 3:
            end_index = end
        else:
            end_index = start + chunk_size
        word_counts.append(count_words(lines[start:end_index]))
        start = end_index
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    chunk_size = int(os.path.getsize(file_path) / 4)
    with ThreadPoolExecutor(max_workers=4) as executor:
        word_counts = list(executor.map(lambda x: process_chunk(file_path, chunk_size), [0]*4))
        for wc in word_counts:
            print(json.dumps(wc, indent=2))

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. import json import os import re from concurrent.futures import ThreadPoolExecutor def count_words(lines): word_count = {} for line in lines: words = re.findall(r'\w+', line) for word in words: word = word.lower() word_count[word] = word_count.get(word, 0) + 1 return word_count def process_chunk(file_path, chunk_size): with open(file_path, 'r') as f: lines = f.readlines() start = 0 end = len(lines) word_counts = [] for i in range(4): if i == 3: end_index = end else: end_index = start + chunk_size word_counts.append(count_words(lines[start:end_index])) start = end_index return word_counts if __name__ == '__main__': file_path = '/tmp/file.txt' chunk_size = int(os.path.getsize(file_path) / 4) with ThreadPoolExecutor(max_workers=4) as executor: word_counts = list(executor.map(lambda x: process_chunk(file_path, chunk_size), [0]*4)) for wc in word_counts: print(json.dumps(wc, indent=2)) 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:

import json
import os
import re
from concurrent.futures import ThreadPoolExecutor

def count_words(lines):
    word_count = {}
    for line in lines:
        words = re.findall(r'\w+', line)
        for word in words:
            word = word.lower()
            word_count[word] = word_count.get(word, 0) + 1
    return word_count

def process_chunk(file_path, chunk_size):
    with open(file_path, 'r') as f:
        lines = f.readlines()
    start = 0
    end = len(lines)
    word_counts = []
    for i in range(4):
        if i == 3:
            end_index = end
        else:
            end_index = start + chunk_size
        word_counts.append(count_words(lines[start:end_index]))
        start = end_index
    return word_counts

if __name__ == '__main__':
    file_path = '/tmp/file.txt'
    chunk_size = int(os.path.getsize(file_path) / 4)
    with ThreadPoolExecutor(max_workers=4) as executor:
        word_counts = list(executor.map(lambda x: process_chunk(file_path, chunk_size), [0]*4))
        for wc in word_counts:
            print(json.dumps(wc, indent=2))

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
  },
  {},
  {},
  {}
]
[
  {
    "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
  },
  {},
  {},
  {}
]
[
  {
    "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
  },
  {},
  {},
  {}
]
[
  {
    "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: {}

Equal Evaluation

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

Here's a Python script that uses the concurrent.futures module to process the file in parallel on 4 cores. The script reads the file in chunks and processes each chunk in a separate thread. The word counts are then printed as separate JSON dictionaries.

import json
import os
import concurrent.futures

def word_count(text):
    counts = {}
    words = text.split()
    for word in words:
        if word in counts:
            counts[word] += 1
        else:
            counts[word] = 1
    return counts

def process_chunk(chunk):
    return word_count(chunk)

if __name__ == "__main__":
    with open("/tmp/file.txt", "r") as f:
        text = f.read()
        chunks = [text[i::4] for i in range(4)]

    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
        futures = [executor.submit(process_chunk, chunk) for chunk in chunks]

        for future in concurrent.futures.as_completed(futures):
            counts = future.result()
            print(json.dumps(counts))

Note that this script does not handle the merging of the dictionaries. The output will be 4 separate JSON dictionaries, one per line, each containing the word counts for its corresponding chunk of the input file. You can then merge these dictionaries using a separate script or tool. Also note that this script assumes that the input file is small enough to fit into memory. If the file is very large, you may need to modify the script to process it in smaller chunks or use a different approach.

Extract Code

I extracted the following code from that output:

import json
import os
import concurrent.futures

def word_count(text):
    counts = {}
    words = text.split()
    for word in words:
        if word in counts:
            counts[word] += 1
        else:
            counts[word] = 1
    return counts

def process_chunk(chunk):
    return word_count(chunk)

if __name__ == "__main__":
    with open("/tmp/file.txt", "r") as f:
        text = f.read()
        chunks = [text[i::4] for i in range(4)]

    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
        futures = [executor.submit(process_chunk, chunk) for chunk in chunks]

        for future in concurrent.futures.as_completed(futures):
            counts = future.result()
            print(json.dumps(counts))

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. import json import os import concurrent.futures def word_count(text): counts = {} words = text.split() for word in words: if word in counts: counts[word] += 1 else: counts[word] = 1 return counts def process_chunk(chunk): return word_count(chunk) if __name__ == "__main__": with open("/tmp/file.txt", "r") as f: text = f.read() chunks = [text[i::4] for i in range(4)] with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: futures = [executor.submit(process_chunk, chunk) for chunk in chunks] for future in concurrent.futures.as_completed(futures): counts = future.result() print(json.dumps(counts)) 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:

import json
import os
import concurrent.futures

def word_count(text):
    counts = {}
    words = text.split()
    for word in words:
        if word in counts:
            counts[word] += 1
        else:
            counts[word] = 1
    return counts

def process_chunk(chunk):
    return word_count(chunk)

if __name__ == "__main__":
    with open("/tmp/file.txt", "r") as f:
        text = f.read()
        chunks = [text[i::4] for i in range(4)]

    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
        futures = [executor.submit(process_chunk, chunk) for chunk in chunks]

        for future in concurrent.futures.as_completed(futures):
            counts = future.result()
            print(json.dumps(counts))

And got the output:

{"iaheoi,": 1, "sfm": 1, "wtaoimtsee": 1, "ln,": 1, "cfl,": 1, "cfcuytsea": 1, "Lttsea": 1, "Dn,": 1, "ioo": 1, "wtwefsredeh": 1, "ouwaonee,": 1, "elidcoa,": 1, "elidcht": 1}
{"tsesfm": 1, "wtwt": 1, "eiahgfs,": 1, "fie": 1, "wteh": 2, "i": 1, "rl,": 1, "soi,": 1, "soae": 1, "wtsnfpiahir": 1, "p,": 1, "ribrsedtgf": 1, "w": 2, "nit": 1, "v": 1, "nitehw": 1}
{"t": 1, "eiaho": 1, "tstsee": 1, "d": 1, "wtaoossiahp": 1, "beiahp": 1, "iei": 1, "wtsofg": 1, "wtsofrsiahpg": 1, "etsen": 1, "da": 1, "heynee,": 1, "h": 1, "ouweaggr": 1, "Heweaggr": 1, "ea": 1}
{"wtb": 1, "tstseroi,": 1, "woiahgfohstseooeftseoondtiahen": 1, "hiahen": 1, "kstser": 1, "h,": 1, "toeiwavtgf": 1, "hnibrserlo": 1, "etenerlo": 1, "etory": 1}

PyFunc

Resulting in output: {'iaheoi,': 1, 'sfm': 1, 'wtaoimtsee': 1, 'ln,': 1, 'cfl,': 1, 'cfcuytsea': 1, 'Lttsea': 1, 'Dn,': 1, 'ioo': 1, 'wtwefsredeh': 1, 'ouwaonee,': 1, 'elidcoa,': 1, 'elidcht': 1, 'tsesfm': 1, 'wtwt': 1, 'eiahgfs,': 1, 'fie': 1, 'wteh': 2, 'i': 1, 'rl,': 1, 'soi,': 1, 'soae': 1, 'wtsnfpiahir': 1, 'p,': 1, 'ribrsedtgf': 1, 'w': 2, 'nit': 1, 'v': 1, 'nitehw': 1, 't': 1, 'eiaho': 1, 'tstsee': 1, 'd': 1, 'wtaoossiahp': 1, 'beiahp': 1, 'iei': 1, 'wtsofg': 1, 'wtsofrsiahpg': 1, 'etsen': 1, 'da': 1, 'heynee,': 1, 'h': 1, 'ouweaggr': 1, 'Heweaggr': 1, 'ea': 1, 'wtb': 1, 'tstseroi,': 1, 'woiahgfohstseooeftseoondtiahen': 1, 'hiahen': 1, 'kstser': 1, 'h,': 1, 'toeiwavtgf': 1, 'hnibrserlo': 1, 'etenerlo': 1, 'etory': 1}