Docker Setup

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

Initial Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

LLM Generation

Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

Output

# Python Script to Merge Files into 16 Equal-Sized Files Here's a Python script that reads file paths from `/tmp/files` and merges their contents into 16 approximately equal-sized files: 

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#!/usr/bin/env python3
import os

def merge_files():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f.readlines()]
    
    # Get the size of each file
    file_info = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            file_info.append((path, size))
        except (FileNotFoundError, PermissionError) as e:
            print(f"Warning: Could not access {path}: {e}")
    
    # Initialize the 16 output files
    output_files = [[] for _ in range(16)]
    output_sizes = [0] * 16
    
    # Sort files by size in descending order for better distribution
    file_info.sort(key=lambda x: x[1], reverse=True)
    
    # Distribute files using greedy algorithm (add each file to the smallest output)
    for path, size in file_info:
        # Find the output file with the smallest current size
        smallest_idx = output_sizes.index(min(output_sizes))
        output_files[smallest_idx].append(path)
        output_sizes[smallest_idx] += size
    
    # Write the content to the merged files
    for i in range(16):
        output_path = f'/tmp/merged_{i}'
        try:
            with open(output_path, 'wb') as out_file:
                for input_path in output_files[i]:
                    try:
                        with open(input_path, 'rb') as in_file:
                            out_file.write(in_file.read())
                    except Exception as e:
                        print(f"Error reading {input_path}: {e}")
            print(f"Created {output_path} with {len(output_files[i])} files, {output_sizes[i]} bytes")
        except Exception as e:
            print(f"Error creating {output_path}: {e}")

if __name__ == "__main__":
    merge_files()
    print("Merging completed successfully.")
## How This Script Works 1. Reads the list of file paths from `/tmp/files` 2. Calculates the size of each file 3. Uses a greedy algorithm to distribute files among the 16 output files: - Always adds the next file to the output file that currently has the smallest total size - Sorts files by size first (largest to smallest) for better balancing 4. Writes the contents of each group of files to its respective output file 5. Provides progress information and error handling This approach ensures that the 16 output files will be as close as possible to the same size.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import os

def merge_files():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f.readlines()]
    
    # Get the size of each file
    file_info = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            file_info.append((path, size))
        except (FileNotFoundError, PermissionError) as e:
            print(f"Warning: Could not access {path}: {e}")
    
    # Initialize the 16 output files
    output_files = [[] for _ in range(16)]
    output_sizes = [0] * 16
    
    # Sort files by size in descending order for better distribution
    file_info.sort(key=lambda x: x[1], reverse=True)
    
    # Distribute files using greedy algorithm (add each file to the smallest output)
    for path, size in file_info:
        # Find the output file with the smallest current size
        smallest_idx = output_sizes.index(min(output_sizes))
        output_files[smallest_idx].append(path)
        output_sizes[smallest_idx] += size
    
    # Write the content to the merged files
    for i in range(16):
        output_path = f'/tmp/merged_{i}'
        try:
            with open(output_path, 'wb') as out_file:
                for input_path in output_files[i]:
                    try:
                        with open(input_path, 'rb') as in_file:
                            out_file.write(in_file.read())
                    except Exception as e:
                        print(f"Error reading {input_path}: {e}")
            print(f"Created {output_path} with {len(output_files[i])} files, {output_sizes[i]} bytes")
        except Exception as e:
            print(f"Error creating {output_path}: {e}")

if __name__ == "__main__":
    merge_files()
    print("Merging completed successfully.")

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3
import os

def merge_files():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f.readlines()]
    
    # Get the size of each file
    file_info = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            file_info.append((path, size))
        except (FileNotFoundError, PermissionError) as e:
            print(f"Warning: Could not access {path}: {e}")
    
    # Initialize the 16 output files
    output_files = [[] for _ in range(16)]
    output_sizes = [0] * 16
    
    # Sort files by size in descending order for better distribution
    file_info.sort(key=lambda x: x[1], reverse=True)
    
    # Distribute files using greedy algorithm (add each file to the smallest output)
    for path, size in file_info:
        # Find the output file with the smallest current size
        smallest_idx = output_sizes.index(min(output_sizes))
        output_files[smallest_idx].append(path)
        output_sizes[smallest_idx] += size
    
    # Write the content to the merged files
    for i in range(16):
        output_path = f'/tmp/merged_{i}'
        try:
            with open(output_path, 'wb') as out_file:
                for input_path in output_files[i]:
                    try:
                        with open(input_path, 'rb') as in_file:
                            out_file.write(in_file.read())
                    except Exception as e:
                        print(f"Error reading {input_path}: {e}")
            print(f"Created {output_path} with {len(output_files[i])} files, {output_sizes[i]} bytes")
        except Exception as e:
            print(f"Error creating {output_path}: {e}")

if __name__ == "__main__":
    merge_files()
    print("Merging completed successfully.")
And got the output: Created /tmp/merged_0 with 36 files, 37598 bytes Created /tmp/merged_1 with 37 files, 37588 bytes Created /tmp/merged_2 with 46 files, 37590 bytes Created /tmp/merged_3 with 50 files, 37588 bytes Created /tmp/merged_4 with 60 files, 37593 bytes Created /tmp/merged_5 with 70 files, 37588 bytes Created /tmp/merged_6 with 70 files, 37592 bytes Created /tmp/merged_7 with 70 files, 37596 bytes Created /tmp/merged_8 with 70 files, 37587 bytes Created /tmp/merged_9 with 70 files, 37588 bytes Created /tmp/merged_10 with 70 files, 37587 bytes Created /tmp/merged_11 with 71 files, 37589 bytes Created /tmp/merged_12 with 70 files, 37592 bytes Created /tmp/merged_13 with 70 files, 37587 bytes Created /tmp/merged_14 with 70 files, 37597 bytes Created /tmp/merged_15 with 70 files, 37589 bytes Merging completed successfully.

PyFunc

final: True Resulting in output: True

Docker Setup

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

Initial Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

LLM Generation

Query

I have a list of several thousand file paths in /tmp/files. Write a python script that merges the content int 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. "/tmp/files" is a list of file paths, one per line.

Output

# File Merger Script Here's a Python script that merges the contents from a list of files into 16 approximately equally-sized files: 

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#!/usr/bin/env python3
import os

def merge_files():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Calculate the size of each file
    file_sizes = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            file_sizes.append((path, size))
        except (FileNotFoundError, PermissionError) as e:
            print(f"Warning: Could not access {path}: {e}")
    
    # Sort files by size (largest first) for better distribution
    file_sizes.sort(key=lambda x: x[1], reverse=True)
    
    # Initialize buckets to distribute files
    buckets = [[] for _ in range(16)]
    bucket_sizes = [0] * 16
    
    # Distribute files into buckets to balance the sizes
    for path, size in file_sizes:
        # Add each file to the bucket with the smallest current size
        smallest_bucket = min(range(16), key=lambda i: bucket_sizes[i])
        buckets[smallest_bucket].append(path)
        bucket_sizes[smallest_bucket] += size
    
    # Merge files in each bucket
    for i, bucket in enumerate(buckets):
        output_path = f'/tmp/merged_{i}'
        with open(output_path, 'wb') as out_file:
            for file_path in bucket:
                try:
                    with open(file_path, 'rb') as in_file:
                        # Read and write in chunks to avoid memory issues
                        chunk_size = 8 * 1024 * 1024  # 8MB chunks
                        while True:
                            chunk = in_file.read(chunk_size)
                            if not chunk:
                                break
                            out_file.write(chunk)
                except (FileNotFoundError, PermissionError) as e:
                    print(f"Warning: Could not read {file_path}: {e}")
        
        print(f"Created {output_path} with {len(bucket)} files, total size: {bucket_sizes[i]} bytes")

if __name__ == "__main__":
    merge_files()
## How it works: 1. The script reads the list of file paths from `/tmp/files` 2. It calculates the size of each file 3. Files are sorted by size (largest first) to help balance the distribution 4. Files are distributed into 16 buckets using a greedy algorithm, always adding the next file to the bucket with the smallest current size 5. For each bucket, the script merges the files into the corresponding output file (`/tmp/merged_0` through `/tmp/merged_15`) 6. File content is read and written in 8MB chunks to handle large files efficiently without exhausting memory This approach ensures that each of the 16 files will be approximately the same size.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import os

def merge_files():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Calculate the size of each file
    file_sizes = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            file_sizes.append((path, size))
        except (FileNotFoundError, PermissionError) as e:
            print(f"Warning: Could not access {path}: {e}")
    
    # Sort files by size (largest first) for better distribution
    file_sizes.sort(key=lambda x: x[1], reverse=True)
    
    # Initialize buckets to distribute files
    buckets = [[] for _ in range(16)]
    bucket_sizes = [0] * 16
    
    # Distribute files into buckets to balance the sizes
    for path, size in file_sizes:
        # Add each file to the bucket with the smallest current size
        smallest_bucket = min(range(16), key=lambda i: bucket_sizes[i])
        buckets[smallest_bucket].append(path)
        bucket_sizes[smallest_bucket] += size
    
    # Merge files in each bucket
    for i, bucket in enumerate(buckets):
        output_path = f'/tmp/merged_{i}'
        with open(output_path, 'wb') as out_file:
            for file_path in bucket:
                try:
                    with open(file_path, 'rb') as in_file:
                        # Read and write in chunks to avoid memory issues
                        chunk_size = 8 * 1024 * 1024  # 8MB chunks
                        while True:
                            chunk = in_file.read(chunk_size)
                            if not chunk:
                                break
                            out_file.write(chunk)
                except (FileNotFoundError, PermissionError) as e:
                    print(f"Warning: Could not read {file_path}: {e}")
        
        print(f"Created {output_path} with {len(bucket)} files, total size: {bucket_sizes[i]} bytes")

if __name__ == "__main__":
    merge_files()

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3
import os

def merge_files():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Calculate the size of each file
    file_sizes = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            file_sizes.append((path, size))
        except (FileNotFoundError, PermissionError) as e:
            print(f"Warning: Could not access {path}: {e}")
    
    # Sort files by size (largest first) for better distribution
    file_sizes.sort(key=lambda x: x[1], reverse=True)
    
    # Initialize buckets to distribute files
    buckets = [[] for _ in range(16)]
    bucket_sizes = [0] * 16
    
    # Distribute files into buckets to balance the sizes
    for path, size in file_sizes:
        # Add each file to the bucket with the smallest current size
        smallest_bucket = min(range(16), key=lambda i: bucket_sizes[i])
        buckets[smallest_bucket].append(path)
        bucket_sizes[smallest_bucket] += size
    
    # Merge files in each bucket
    for i, bucket in enumerate(buckets):
        output_path = f'/tmp/merged_{i}'
        with open(output_path, 'wb') as out_file:
            for file_path in bucket:
                try:
                    with open(file_path, 'rb') as in_file:
                        # Read and write in chunks to avoid memory issues
                        chunk_size = 8 * 1024 * 1024  # 8MB chunks
                        while True:
                            chunk = in_file.read(chunk_size)
                            if not chunk:
                                break
                            out_file.write(chunk)
                except (FileNotFoundError, PermissionError) as e:
                    print(f"Warning: Could not read {file_path}: {e}")
        
        print(f"Created {output_path} with {len(bucket)} files, total size: {bucket_sizes[i]} bytes")

if __name__ == "__main__":
    merge_files()
And got the output: Created /tmp/merged_0 with 39 files, total size: 36316 bytes Created /tmp/merged_1 with 45 files, total size: 36322 bytes Created /tmp/merged_2 with 54 files, total size: 36322 bytes Created /tmp/merged_3 with 55 files, total size: 36322 bytes Created /tmp/merged_4 with 57 files, total size: 36322 bytes Created /tmp/merged_5 with 68 files, total size: 36317 bytes Created /tmp/merged_6 with 68 files, total size: 36319 bytes Created /tmp/merged_7 with 68 files, total size: 36319 bytes Created /tmp/merged_8 with 69 files, total size: 36316 bytes Created /tmp/merged_9 with 69 files, total size: 36316 bytes Created /tmp/merged_10 with 68 files, total size: 36319 bytes Created /tmp/merged_11 with 68 files, total size: 36316 bytes Created /tmp/merged_12 with 68 files, total size: 36316 bytes Created /tmp/merged_13 with 68 files, total size: 36319 bytes Created /tmp/merged_14 with 68 files, total size: 36318 bytes Created /tmp/merged_15 with 68 files, total size: 36319 bytes

PyFunc

final: True Resulting in output: True