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

import json
import multiprocessing
from pathlib import Path

CORES = 4


def count_words(lines):
    """Counts the words in a list of lines.

    Args:
        lines: A list of lines from a text file.

    Returns:
        A dictionary of word counts.
    """
    word_counts = {}
    for line in lines:
        words = line.split()
        for word in words:
            word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts


def main():
    """Counts the words in a text file and prints the results as a JSON object."""
    # Read the text file
    with open("/tmp/file.txt", "r") as f:
        lines = f.readlines()

    # Split the lines into chunks
    chunks = [lines[i::CORES] for i in range(CORES)]

    # Create a pool of workers
    pool = multiprocessing.Pool(CORES)

    # Count the words in each chunk
    results = pool.map(count_words, chunks)

    # Merge the results
    word_counts = {}
    for result in results:
        for word, count in result.items():
            word_counts[word] = word_counts.get(word, 0) + count

    # Print the results as a JSON object
    print(json.dumps(word_counts))


if __name__ == "__main__":
    main()

Extract Code

I extracted the following code from that output:

import json
import multiprocessing
from pathlib import Path

CORES = 4


def count_words(lines):
    """Counts the words in a list of lines.

    Args:
        lines: A list of lines from a text file.

    Returns:
        A dictionary of word counts.
    """
    word_counts = {}
    for line in lines:
        words = line.split()
        for word in words:
            word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts


def main():
    """Counts the words in a text file and prints the results as a JSON object."""
    # Read the text file
    with open("/tmp/file.txt", "r") as f:
        lines = f.readlines()

    # Split the lines into chunks
    chunks = [lines[i::CORES] for i in range(CORES)]

    # Create a pool of workers
    pool = multiprocessing.Pool(CORES)

    # Count the words in each chunk
    results = pool.map(count_words, chunks)

    # Merge the results
    word_counts = {}
    for result in results:
        for word, count in result.items():
            word_counts[word] = word_counts.get(word, 0) + count

    # Print the results as a JSON object
    print(json.dumps(word_counts))


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. import json import multiprocessing from pathlib import Path CORES = 4 def count_words(lines): """Counts the words in a list of lines. Args: lines: A list of lines from a text file. Returns: A dictionary of word counts. """ word_counts = {} for line in lines: words = line.split() for word in words: word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def main(): """Counts the words in a text file and prints the results as a JSON object.""" # Read the text file with open("/tmp/file.txt", "r") as f: lines = f.readlines() # Split the lines into chunks chunks = [lines[i::CORES] for i in range(CORES)] # Create a pool of workers pool = multiprocessing.Pool(CORES) # Count the words in each chunk results = pool.map(count_words, chunks) # Merge the results word_counts = {} for result in results: for word, count in result.items(): word_counts[word] = word_counts.get(word, 0) + count # Print the results as a JSON object print(json.dumps(word_counts)) 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:

import json
import multiprocessing
from pathlib import Path

CORES = 4


def count_words(lines):
    """Counts the words in a list of lines.

    Args:
        lines: A list of lines from a text file.

    Returns:
        A dictionary of word counts.
    """
    word_counts = {}
    for line in lines:
        words = line.split()
        for word in words:
            word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts


def main():
    """Counts the words in a text file and prints the results as a JSON object."""
    # Read the text file
    with open("/tmp/file.txt", "r") as f:
        lines = f.readlines()

    # Split the lines into chunks
    chunks = [lines[i::CORES] for i in range(CORES)]

    # Create a pool of workers
    pool = multiprocessing.Pool(CORES)

    # Count the words in each chunk
    results = pool.map(count_words, chunks)

    # Merge the results
    word_counts = {}
    for result in results:
        for word, count in result.items():
            word_counts[word] = word_counts.get(word, 0) + count

    # Print the results as a JSON object
    print(json.dumps(word_counts))


if __name__ == "__main__":
    main()

And got the output:

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

PyFunc

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

Equal Evaluation

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

Final Answer: True

Docker Setup

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

Initial Query

LLM Generation

Query

Output

from concurrent.futures import ProcessPoolExecutor, as_completed
import json
import os

def count_words(filename, start, end):
  with open(filename, 'r') as f:
    words = f.read()[start:end].split()
  word_counts = {}
  for word in words:
    word_counts[word] = word_counts.get(word, 0) + 1
  return word_counts

def main():
  filename = '/tmp/file.txt'
  filesize = os.path.getsize(filename)
  with ProcessPoolExecutor(max_workers=4) as executor:
    futures = []
    for i in range(4):
      start = i * filesize // 4
      end = (i + 1) * filesize // 4
      futures.append(executor.submit(count_words, filename, start, end))
    for future in as_completed(futures):
      print(json.dumps(future.result()))

if __name__ == '__main__':
  main()

Extract Code

I extracted the following code from that output:

from concurrent.futures import ProcessPoolExecutor, as_completed
import json
import os

def count_words(filename, start, end):
  with open(filename, 'r') as f:
    words = f.read()[start:end].split()
  word_counts = {}
  for word in words:
    word_counts[word] = word_counts.get(word, 0) + 1
  return word_counts

def main():
  filename = '/tmp/file.txt'
  filesize = os.path.getsize(filename)
  with ProcessPoolExecutor(max_workers=4) as executor:
    futures = []
    for i in range(4):
      start = i * filesize // 4
      end = (i + 1) * filesize // 4
      futures.append(executor.submit(count_words, filename, start, end))
    for future in as_completed(futures):
      print(json.dumps(future.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. from concurrent.futures import ProcessPoolExecutor, as_completed import json import os def count_words(filename, start, end): with open(filename, 'r') as f: words = f.read()[start:end].split() word_counts = {} for word in words: word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def main(): filename = '/tmp/file.txt' filesize = os.path.getsize(filename) with ProcessPoolExecutor(max_workers=4) as executor: futures = [] for i in range(4): start = i * filesize // 4 end = (i + 1) * filesize // 4 futures.append(executor.submit(count_words, filename, start, end)) for future in as_completed(futures): print(json.dumps(future.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:

from concurrent.futures import ProcessPoolExecutor, as_completed
import json
import os

def count_words(filename, start, end):
  with open(filename, 'r') as f:
    words = f.read()[start:end].split()
  word_counts = {}
  for word in words:
    word_counts[word] = word_counts.get(word, 0) + 1
  return word_counts

def main():
  filename = '/tmp/file.txt'
  filesize = os.path.getsize(filename)
  with ProcessPoolExecutor(max_workers=4) as executor:
    futures = []
    for i in range(4):
      start = i * filesize // 4
      end = (i + 1) * filesize // 4
      futures.append(executor.submit(count_words, filename, start, end))
    for future in as_completed(futures):
      print(json.dumps(future.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}
{"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

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

from multiprocessing import Pool
import json
import os

def count_words(filename):
    with open(filename) as f:
        words = f.read().split()
    return {word: words.count(word) for word in words}

if __name__ == "__main__":
    with open('/tmp/file.txt', 'r') as f:
        text = f.read()
    
    # Split the text into 4 equal parts
    parts = [text[i:i+len(text)//4] for i in range(0, len(text), len(text)//4)]
    
    # Create a pool of 4 workers
    pool = Pool(4)
    
    # Count the words in each part of the text in parallel
    counts = pool.map(count_words, parts)
    
    # Print the results as JSON
    for count in counts:
        print(json.dumps(count))

Extract Code

I extracted the following code from that output:

from multiprocessing import Pool
import json
import os

def count_words(filename):
    with open(filename) as f:
        words = f.read().split()
    return {word: words.count(word) for word in words}

if __name__ == "__main__":
    with open('/tmp/file.txt', 'r') as f:
        text = f.read()
    
    # Split the text into 4 equal parts
    parts = [text[i:i+len(text)//4] for i in range(0, len(text), len(text)//4)]
    
    # Create a pool of 4 workers
    pool = Pool(4)
    
    # Count the words in each part of the text in parallel
    counts = pool.map(count_words, parts)
    
    # Print the results as JSON
    for count in counts:
        print(json.dumps(count))

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. from multiprocessing import Pool import json import os def count_words(filename): with open(filename) as f: words = f.read().split() return {word: words.count(word) for word in words} if __name__ == "__main__": with open('/tmp/file.txt', 'r') as f: text = f.read() # Split the text into 4 equal parts parts = [text[i:i+len(text)//4] for i in range(0, len(text), len(text)//4)] # Create a pool of 4 workers pool = Pool(4) # Count the words in each part of the text in parallel counts = pool.map(count_words, parts) # Print the results as JSON for count in counts: print(json.dumps(count)) 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:

from multiprocessing import Pool
import json
import os

def count_words(filename):
    with open(filename) as f:
        words = f.read().split()
    return {word: words.count(word) for word in words}

if __name__ == "__main__":
    with open('/tmp/file.txt', 'r') as f:
        text = f.read()
    
    # Split the text into 4 equal parts
    parts = [text[i:i+len(text)//4] for i in range(0, len(text), len(text)//4)]
    
    # Create a pool of 4 workers
    pool = Pool(4)
    
    # Count the words in each part of the text in parallel
    counts = pool.map(count_words, parts)
    
    # Print the results as JSON
    for count in counts:
        print(json.dumps(count))

And got the output:

multiprocessing.pool.RemoteTraceback: 
"""
Traceback (most recent call last):
  File "/usr/lib/python3.11/multiprocessing/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
                    ^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/multiprocessing/pool.py", line 48, in mapstar
    return list(map(*args))
           ^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 7, in count_words
    with open(filename) as f:
         ^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'it was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolish'
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/usr/src/app/main.py", line 22, in <module>
    counts = pool.map(count_words, parts)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/multiprocessing/pool.py", line 367, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/multiprocessing/pool.py", line 774, in get
    raise self._value
FileNotFoundError: [Errno 2] No such file or directory: 'it was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolish'

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

import json
import multiprocessing
import os

def count_words(file_path, start, end):
    with open(file_path, "r") as f:
        words = f.read()[start:end].split()
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def main():
    file_path = "/tmp/file.txt"
    file_size = os.path.getsize(file_path)
    num_cores = 4
    chunk_size = file_size // num_cores

    # Create a pool of workers
    pool = multiprocessing.Pool(num_cores)

    # Divide the file into chunks and assign each chunk to a worker
    chunks = [(file_path, i * chunk_size, (i + 1) * chunk_size) for i in range(num_cores)]
    results = pool.starmap(count_words, chunks)

    # Merge the results from each worker
    word_counts = {}
    for result in results:
        for word, count in result.items():
            word_counts[word] = word_counts.get(word, 0) + count

    # Print the word counts in JSON format
    for word, count in word_counts.items():
        print(json.dumps({word: count}))

if __name__ == "__main__":
    main()

Extract Code

I extracted the following code from that output:

import json
import multiprocessing
import os

def count_words(file_path, start, end):
    with open(file_path, "r") as f:
        words = f.read()[start:end].split()
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def main():
    file_path = "/tmp/file.txt"
    file_size = os.path.getsize(file_path)
    num_cores = 4
    chunk_size = file_size // num_cores

    # Create a pool of workers
    pool = multiprocessing.Pool(num_cores)

    # Divide the file into chunks and assign each chunk to a worker
    chunks = [(file_path, i * chunk_size, (i + 1) * chunk_size) for i in range(num_cores)]
    results = pool.starmap(count_words, chunks)

    # Merge the results from each worker
    word_counts = {}
    for result in results:
        for word, count in result.items():
            word_counts[word] = word_counts.get(word, 0) + count

    # Print the word counts in JSON format
    for word, count in word_counts.items():
        print(json.dumps({word: count}))

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. import json import multiprocessing import os def count_words(file_path, start, end): with open(file_path, "r") as f: words = f.read()[start:end].split() word_counts = {} for word in words: word_counts[word] = word_counts.get(word, 0) + 1 return word_counts def main(): file_path = "/tmp/file.txt" file_size = os.path.getsize(file_path) num_cores = 4 chunk_size = file_size // num_cores # Create a pool of workers pool = multiprocessing.Pool(num_cores) # Divide the file into chunks and assign each chunk to a worker chunks = [(file_path, i * chunk_size, (i + 1) * chunk_size) for i in range(num_cores)] results = pool.starmap(count_words, chunks) # Merge the results from each worker word_counts = {} for result in results: for word, count in result.items(): word_counts[word] = word_counts.get(word, 0) + count # Print the word counts in JSON format for word, count in word_counts.items(): print(json.dumps({word: count})) 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:

import json
import multiprocessing
import os

def count_words(file_path, start, end):
    with open(file_path, "r") as f:
        words = f.read()[start:end].split()
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

def main():
    file_path = "/tmp/file.txt"
    file_size = os.path.getsize(file_path)
    num_cores = 4
    chunk_size = file_size // num_cores

    # Create a pool of workers
    pool = multiprocessing.Pool(num_cores)

    # Divide the file into chunks and assign each chunk to a worker
    chunks = [(file_path, i * chunk_size, (i + 1) * chunk_size) for i in range(num_cores)]
    results = pool.starmap(count_words, chunks)

    # Merge the results from each worker
    word_counts = {}
    for result in results:
        for word, count in result.items():
            word_counts[word] = word_counts.get(word, 0) + count

    # Print the word counts in JSON format
    for word, count in word_counts.items():
        print(json.dumps({word: count}))

if __name__ == "__main__":
    main()

And got the 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}

PyFunc

Equal Evaluation

Final Answer: False

Docker Setup

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

Initial Query

LLM Generation

Query

Output

import json
import multiprocessing
import os

def word_count(file_name, start, end):
    with open(file_name, "r") as f:
        words = f.read()[start:end].split()
    counts = {}
    for word in words:
        word = word.strip(".,!?").lower()
        if word:
            counts[word] = counts.get(word, 0) + 1
    return counts

if __name__ == "__main__":
    file_name = "/tmp/file.txt"
    file_size = os.path.getsize(file_name)
    num_cores = 4
    chunk_size = file_size // num_cores

    with multiprocessing.Pool(num_cores) as pool:
        results = pool.starmap(word_count, [(file_name, i * chunk_size, (i + 1) * chunk_size) for i in range(num_cores)])

    for result in results:
        print(json.dumps(result))

Extract Code

I extracted the following code from that output:

import json
import multiprocessing
import os

def word_count(file_name, start, end):
    with open(file_name, "r") as f:
        words = f.read()[start:end].split()
    counts = {}
    for word in words:
        word = word.strip(".,!?").lower()
        if word:
            counts[word] = counts.get(word, 0) + 1
    return counts

if __name__ == "__main__":
    file_name = "/tmp/file.txt"
    file_size = os.path.getsize(file_name)
    num_cores = 4
    chunk_size = file_size // num_cores

    with multiprocessing.Pool(num_cores) as pool:
        results = pool.starmap(word_count, [(file_name, i * chunk_size, (i + 1) * chunk_size) for i in range(num_cores)])

    for result in results:
        print(json.dumps(result))

Check if all of the following conditions are true:

LLM Generation

Query

Below is some student's code. I asked them to parallelize some code. Do not judge if it is correct, I have test cases for that. Just tell me if they tried to parallelize it. import json import multiprocessing import os def word_count(file_name, start, end): with open(file_name, "r") as f: words = f.read()[start:end].split() counts = {} for word in words: word = word.strip(".,!?").lower() if word: counts[word] = counts.get(word, 0) + 1 return counts if __name__ == "__main__": file_name = "/tmp/file.txt" file_size = os.path.getsize(file_name) num_cores = 4 chunk_size = file_size // num_cores with multiprocessing.Pool(num_cores) as pool: results = pool.starmap(word_count, [(file_name, i * chunk_size, (i + 1) * chunk_size) for i in range(num_cores)]) 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:

import json
import multiprocessing
import os

def word_count(file_name, start, end):
    with open(file_name, "r") as f:
        words = f.read()[start:end].split()
    counts = {}
    for word in words:
        word = word.strip(".,!?").lower()
        if word:
            counts[word] = counts.get(word, 0) + 1
    return counts

if __name__ == "__main__":
    file_name = "/tmp/file.txt"
    file_size = os.path.getsize(file_name)
    num_cores = 4
    chunk_size = file_size // num_cores

    with multiprocessing.Pool(num_cores) as pool:
        results = pool.starmap(word_count, [(file_name, i * chunk_size, (i + 1) * chunk_size) for i in range(num_cores)])

    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}