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

To solve this problem, we need to write a Python program that counts the frequency of each word in a text file located at `/tmp/file.txt` and prints the results as four separate JSON dictionaries, each processed in parallel by one of four cores. Each core handles a different chunk of the file, ensuring that words are not split across chunks. ### Approach 1. **File Splitting**: The file is divided into four chunks based on byte positions. Each chunk is adjusted to start and end at word boundaries (whitespace characters) to avoid splitting words. 2. **Parallel Processing**: Each chunk is processed in parallel using Python's `multiprocessing.Pool` to utilize four cores efficiently. 3. **Word Counting**: Each chunk is read, decoded, split into words, and the frequency of each word is counted. 4. **JSON Output**: The word counts from each chunk are printed as separate JSON dictionaries, one per line. ### Solution Code 

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#!/usr/bin/env python3

import json
from multiprocessing import Pool

def find_word_boundary(file, position, direction='forward'):
    whitespace = {b' ', b'\t', b'\n', b'\r', b'\x0b', b'\x0c'}
    chunk_size = 1024
    file.seek(position)
    if direction == 'forward':
        while True:
            data = file.read(chunk_size)
            if not data:
                return file.tell()
            for i, byte in enumerate(data):
                if bytes([byte]) in whitespace:
                    return position + i + 1
            position += len(data)
        return file.tell()
    elif direction == 'backward':
        pos = position - 1
        while pos >= 0:
            file.seek(pos)
            byte = file.read(1)
            if not byte:
                break
            if byte in whitespace:
                return pos + 1
            pos -= 1
        return 0
    else:
        raise ValueError("Invalid direction")

def process_chunk(args):
    start, end = args
    counts = {}
    with open('/tmp/file.txt', 'rb') as f:
        f.seek(start)
        data = f.read(end - start)
    try:
        text = data.decode('utf-8')
    except UnicodeDecodeError:
        text = data.decode('utf-8', errors='replace')
    words = text.split()
    for word in words:
        counts[word] = counts.get(word, 0) + 1
    return counts

if __name__ == '__main__':
    chunks = []
    with open('/tmp/file.txt', 'rb') as f:
        f.seek(0, 2)
        total_size = f.tell()
        if total_size == 0:
            print(json.dumps({}))
            print(json.dumps({}))
            print(json.dumps({}))
            print(json.dumps({}))
            exit()
        chunk_size = total_size // 4
        initial_ends = [(i + 1) * chunk_size for i in range(3)] + [total_size]
        adjusted_ends = []
        for i in range(3):
            end = initial_ends[i]
            with open('/tmp/file.txt', 'rb') as f_in:
                adjusted_end = find_word_boundary(f_in, end, direction='forward')
            adjusted_ends.append(adjusted_end)
        adjusted_ends.append(total_size)
        start = 0
        for end in adjusted_ends:
            chunks.append((start, end))
            start = end

    with Pool(4) as pool:
        results = pool.map(process_chunk, chunks)
    
    for res in results:
        print(json.dumps(res))
### Explanation 1. **File Splitting**: The file is opened in binary mode to accurately determine byte positions. The file size is divided into four initial chunks. Each chunk's end is adjusted to the nearest whitespace to ensure it ends at a word boundary. 2. **Adjusting Boundaries**: The `find_word_boundary` function adjusts the start and end positions of each chunk to avoid splitting words. It reads forward or backward from a given position to find the nearest whitespace. 3. **Parallel Processing**: Using `multiprocessing.Pool`, each chunk is processed in parallel. The `process_chunk` function reads each chunk, decodes it, splits it into words, and counts their occurrences. 4. **Output**: The results from each chunk are printed as JSON dictionaries, one per line, which can be merged externally. This approach ensures efficient parallel processing while maintaining correct word boundaries, providing accurate word counts for each chunk of the file.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3

import json
from multiprocessing import Pool

def find_word_boundary(file, position, direction='forward'):
    whitespace = {b' ', b'\t', b'\n', b'\r', b'\x0b', b'\x0c'}
    chunk_size = 1024
    file.seek(position)
    if direction == 'forward':
        while True:
            data = file.read(chunk_size)
            if not data:
                return file.tell()
            for i, byte in enumerate(data):
                if bytes([byte]) in whitespace:
                    return position + i + 1
            position += len(data)
        return file.tell()
    elif direction == 'backward':
        pos = position - 1
        while pos >= 0:
            file.seek(pos)
            byte = file.read(1)
            if not byte:
                break
            if byte in whitespace:
                return pos + 1
            pos -= 1
        return 0
    else:
        raise ValueError("Invalid direction")

def process_chunk(args):
    start, end = args
    counts = {}
    with open('/tmp/file.txt', 'rb') as f:
        f.seek(start)
        data = f.read(end - start)
    try:
        text = data.decode('utf-8')
    except UnicodeDecodeError:
        text = data.decode('utf-8', errors='replace')
    words = text.split()
    for word in words:
        counts[word] = counts.get(word, 0) + 1
    return counts

if __name__ == '__main__':
    chunks = []
    with open('/tmp/file.txt', 'rb') as f:
        f.seek(0, 2)
        total_size = f.tell()
        if total_size == 0:
            print(json.dumps({}))
            print(json.dumps({}))
            print(json.dumps({}))
            print(json.dumps({}))
            exit()
        chunk_size = total_size // 4
        initial_ends = [(i + 1) * chunk_size for i in range(3)] + [total_size]
        adjusted_ends = []
        for i in range(3):
            end = initial_ends[i]
            with open('/tmp/file.txt', 'rb') as f_in:
                adjusted_end = find_word_boundary(f_in, end, direction='forward')
            adjusted_ends.append(adjusted_end)
        adjusted_ends.append(total_size)
        start = 0
        for end in adjusted_ends:
            chunks.append((start, end))
            start = end

    with Pool(4) as pool:
        results = pool.map(process_chunk, chunks)
    
    for res in results:
        print(json.dumps(res))

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 from multiprocessing import Pool def find_word_boundary(file, position, direction='forward'): whitespace = {b' ', b'\t', b'\n', b'\r', b'\x0b', b'\x0c'} chunk_size = 1024 file.seek(position) if direction == 'forward': while True: data = file.read(chunk_size) if not data: return file.tell() for i, byte in enumerate(data): if bytes([byte]) in whitespace: return position + i + 1 position += len(data) return file.tell() elif direction == 'backward': pos = position - 1 while pos >= 0: file.seek(pos) byte = file.read(1) if not byte: break if byte in whitespace: return pos + 1 pos -= 1 return 0 else: raise ValueError("Invalid direction") def process_chunk(args): start, end = args counts = {} with open('/tmp/file.txt', 'rb') as f: f.seek(start) data = f.read(end - start) try: text = data.decode('utf-8') except UnicodeDecodeError: text = data.decode('utf-8', errors='replace') words = text.split() for word in words: counts[word] = counts.get(word, 0) + 1 return counts if __name__ == '__main__': chunks = [] with open('/tmp/file.txt', 'rb') as f: f.seek(0, 2) total_size = f.tell() if total_size == 0: print(json.dumps({})) print(json.dumps({})) print(json.dumps({})) print(json.dumps({})) exit() chunk_size = total_size // 4 initial_ends = [(i + 1) * chunk_size for i in range(3)] + [total_size] adjusted_ends = [] for i in range(3): end = initial_ends[i] with open('/tmp/file.txt', 'rb') as f_in: adjusted_end = find_word_boundary(f_in, end, direction='forward') adjusted_ends.append(adjusted_end) adjusted_ends.append(total_size) start = 0 for end in adjusted_ends: chunks.append((start, end)) start = end with Pool(4) as pool: results = pool.map(process_chunk, chunks) for res in results: print(json.dumps(res)) 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
from multiprocessing import Pool

def find_word_boundary(file, position, direction='forward'):
    whitespace = {b' ', b'\t', b'\n', b'\r', b'\x0b', b'\x0c'}
    chunk_size = 1024
    file.seek(position)
    if direction == 'forward':
        while True:
            data = file.read(chunk_size)
            if not data:
                return file.tell()
            for i, byte in enumerate(data):
                if bytes([byte]) in whitespace:
                    return position + i + 1
            position += len(data)
        return file.tell()
    elif direction == 'backward':
        pos = position - 1
        while pos >= 0:
            file.seek(pos)
            byte = file.read(1)
            if not byte:
                break
            if byte in whitespace:
                return pos + 1
            pos -= 1
        return 0
    else:
        raise ValueError("Invalid direction")

def process_chunk(args):
    start, end = args
    counts = {}
    with open('/tmp/file.txt', 'rb') as f:
        f.seek(start)
        data = f.read(end - start)
    try:
        text = data.decode('utf-8')
    except UnicodeDecodeError:
        text = data.decode('utf-8', errors='replace')
    words = text.split()
    for word in words:
        counts[word] = counts.get(word, 0) + 1
    return counts

if __name__ == '__main__':
    chunks = []
    with open('/tmp/file.txt', 'rb') as f:
        f.seek(0, 2)
        total_size = f.tell()
        if total_size == 0:
            print(json.dumps({}))
            print(json.dumps({}))
            print(json.dumps({}))
            print(json.dumps({}))
            exit()
        chunk_size = total_size // 4
        initial_ends = [(i + 1) * chunk_size for i in range(3)] + [total_size]
        adjusted_ends = []
        for i in range(3):
            end = initial_ends[i]
            with open('/tmp/file.txt', 'rb') as f_in:
                adjusted_end = find_word_boundary(f_in, end, direction='forward')
            adjusted_ends.append(adjusted_end)
        adjusted_ends.append(total_size)
        start = 0
        for end in adjusted_ends:
            chunks.append((start, end))
            start = end

    with Pool(4) as pool:
        results = pool.map(process_chunk, chunks)
    
    for res in results:
        print(json.dumps(res))
And got the output: {"it": 4, "was": 4, "the": 4, "best": 1, "of": 4, "times,": 2, "worst": 1, "age": 2, "wisdom,": 1, "foolishness,": 1} {"it": 4, "was": 4, "the": 4, "epoch": 2, "of": 3, "belief,": 1, "incredulity,": 1, "season": 1, "Light,": 1} {"season": 1, "of": 3, "Darkness,": 1, "it": 2, "was": 2, "the": 2, "spring": 1, "hope,": 1, "winter": 1, "despair,": 1, "we": 1, "had": 1, "everything": 1, "before": 1, "us,": 1} {"we": 3, "had": 1, "nothing": 1, "before": 1, "us,": 1, "were": 2, "all": 2, "going": 2, "direct": 2, "to": 1, "Heaven,": 1, "the": 1, "other": 1, "way": 1}

PyFunc

Resulting in output: {'it': 10, 'was': 10, 'the': 11, 'best': 1, 'of': 10, 'times,': 2, 'worst': 1, 'age': 2, 'wisdom,': 1, 'foolishness,': 1, '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