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 these all together and splits the content into 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. To do this, sort files by size (largest first) and add files in order always to the smallest one. "/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 these all together and splits the content into 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. To do this, sort files by size (largest first) and add files in order always to the smallest one. "/tmp/files" is a list of file paths, one per line.

Output

# Python Script to Merge Files with Balanced Distribution 

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#!/usr/bin/env python3
import os
import heapq

def main():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Get size of each file and sort (largest first)
    files_with_sizes = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files_with_sizes.append((size, path))
        except (OSError, IOError) as e:
            print(f"Warning: Could not get size of {path}: {e}")
    
    # Sort by size (largest first)
    files_with_sizes.sort(reverse=True)
    
    # Initialize 16 output files (clean or create them)
    for i in range(16):
        with open(f'/tmp/merged_{i}', 'wb') as f:
            pass
    
    # Initialize min-heap to track output file sizes
    output_files = [(0, i) for i in range(16)]  # (size, index)
    heapq.heapify(output_files)
    
    # Process each input file
    for file_size, file_path in files_with_sizes:
        # Get the smallest output file
        curr_size, idx = heapq.heappop(output_files)
        output_path = f'/tmp/merged_{idx}'
        
        try:
            # Read the content of the input file as binary
            with open(file_path, 'rb') as in_f:
                content = in_f.read()
            
            # Append content to the output file
            with open(output_path, 'ab') as out_f:
                out_f.write(content)
            
            # Update the size of the output file and put it back in the heap
            new_size = curr_size + file_size
            heapq.heappush(output_files, (new_size, idx))
        except Exception as e:
            print(f"Error processing {file_path}: {e}")
            # Put the output file back with its original size
            heapq.heappush(output_files, (curr_size, idx))
    
    # Print final size distribution
    sizes = [size for size, _ in output_files]
    print(f"Files merged successfully into 16 files.")
    print(f"Size distribution: min={min(sizes)}, max={max(sizes)}, diff={max(sizes)-min(sizes)}")

if __name__ == "__main__":
    main()
## How the Script Works 1. **Reads** all file paths from `/tmp/files` 2. **Calculates** the size of each file and sorts them by size (largest first) 3. **Creates** 16 empty output files (`/tmp/merged_0` through `/tmp/merged_15`) 4. **Uses a min-heap** to efficiently track which output file is currently the smallest 5. **Processes files** in order of decreasing size: - Always adds the next file to whichever output file is currently the smallest - This greedy approach helps achieve balanced sizes 6. **Handles errors** gracefully if files can't be read 7. **Reports** the final size distribution statistics This approach ensures that the merged files will be as balanced as possible in terms of size.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import os
import heapq

def main():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Get size of each file and sort (largest first)
    files_with_sizes = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files_with_sizes.append((size, path))
        except (OSError, IOError) as e:
            print(f"Warning: Could not get size of {path}: {e}")
    
    # Sort by size (largest first)
    files_with_sizes.sort(reverse=True)
    
    # Initialize 16 output files (clean or create them)
    for i in range(16):
        with open(f'/tmp/merged_{i}', 'wb') as f:
            pass
    
    # Initialize min-heap to track output file sizes
    output_files = [(0, i) for i in range(16)]  # (size, index)
    heapq.heapify(output_files)
    
    # Process each input file
    for file_size, file_path in files_with_sizes:
        # Get the smallest output file
        curr_size, idx = heapq.heappop(output_files)
        output_path = f'/tmp/merged_{idx}'
        
        try:
            # Read the content of the input file as binary
            with open(file_path, 'rb') as in_f:
                content = in_f.read()
            
            # Append content to the output file
            with open(output_path, 'ab') as out_f:
                out_f.write(content)
            
            # Update the size of the output file and put it back in the heap
            new_size = curr_size + file_size
            heapq.heappush(output_files, (new_size, idx))
        except Exception as e:
            print(f"Error processing {file_path}: {e}")
            # Put the output file back with its original size
            heapq.heappush(output_files, (curr_size, idx))
    
    # Print final size distribution
    sizes = [size for size, _ in output_files]
    print(f"Files merged successfully into 16 files.")
    print(f"Size distribution: min={min(sizes)}, max={max(sizes)}, diff={max(sizes)-min(sizes)}")

if __name__ == "__main__":
    main()

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3
import os
import heapq

def main():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Get size of each file and sort (largest first)
    files_with_sizes = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files_with_sizes.append((size, path))
        except (OSError, IOError) as e:
            print(f"Warning: Could not get size of {path}: {e}")
    
    # Sort by size (largest first)
    files_with_sizes.sort(reverse=True)
    
    # Initialize 16 output files (clean or create them)
    for i in range(16):
        with open(f'/tmp/merged_{i}', 'wb') as f:
            pass
    
    # Initialize min-heap to track output file sizes
    output_files = [(0, i) for i in range(16)]  # (size, index)
    heapq.heapify(output_files)
    
    # Process each input file
    for file_size, file_path in files_with_sizes:
        # Get the smallest output file
        curr_size, idx = heapq.heappop(output_files)
        output_path = f'/tmp/merged_{idx}'
        
        try:
            # Read the content of the input file as binary
            with open(file_path, 'rb') as in_f:
                content = in_f.read()
            
            # Append content to the output file
            with open(output_path, 'ab') as out_f:
                out_f.write(content)
            
            # Update the size of the output file and put it back in the heap
            new_size = curr_size + file_size
            heapq.heappush(output_files, (new_size, idx))
        except Exception as e:
            print(f"Error processing {file_path}: {e}")
            # Put the output file back with its original size
            heapq.heappush(output_files, (curr_size, idx))
    
    # Print final size distribution
    sizes = [size for size, _ in output_files]
    print(f"Files merged successfully into 16 files.")
    print(f"Size distribution: min={min(sizes)}, max={max(sizes)}, diff={max(sizes)-min(sizes)}")

if __name__ == "__main__":
    main()
And got the output: Files merged successfully into 16 files. Size distribution: min=36803, max=36809, diff=6

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 these all together and splits the content into 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. To do this, sort files by size (largest first) and add files in order always to the smallest one. "/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 these all together and splits the content into 16 files called /tmp/merged_%d for i in 0..15 where each is approximately the same size. To do this, sort files by size (largest first) and add files in order always to the smallest one. "/tmp/files" is a list of file paths, one per line.

Output

# File Merger and Distributor Here's a Python script that reads file paths from `/tmp/files`, merges their contents, and distributes them across 16 output files with approximately equal sizes: 

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#!/usr/bin/env python3
import os
import heapq
import sys

def merge_and_distribute_files():
    # Read the list of file paths
    try:
        with open('/tmp/files', 'r') as f:
            file_paths = [line.strip() for line in f.readlines()]
    except Exception as e:
        print(f"Error reading input file: {e}")
        sys.exit(1)

    # Get file sizes and sort by size (largest first)
    file_sizes = []
    for file_path in file_paths:
        try:
            size = os.path.getsize(file_path)
            file_sizes.append((size, file_path))
        except Exception as e:
            print(f"Warning: Couldn't get size of {file_path}: {e}")
    
    if not file_sizes:
        print("No valid files found")
        return
        
    file_sizes.sort(reverse=True)
    
    # Initialize 16 output buckets
    output_files = [f'/tmp/merged_{i}' for i in range(16)]
    output_sizes = [0] * 16
    
    # Min heap to always get the smallest output file
    size_heap = [(0, i) for i in range(16)]
    heapq.heapify(size_heap)
    
    # Track which files go to which output
    file_distribution = [[] for _ in range(16)]
    
    # Distribute files to the output buckets
    for file_size, file_path in file_sizes:
        # Get the smallest output bucket
        current_size, index = heapq.heappop(size_heap)
        
        # Add file to this bucket
        file_distribution[index].append(file_path)
        
        # Update the size and put back in heap
        new_size = current_size + file_size
        output_sizes[index] = new_size
        heapq.heappush(size_heap, (new_size, index))
    
    # Now merge the files into each output file
    for i, files in enumerate(file_distribution):
        try:
            with open(output_files[i], 'w') as out_file:
                for file_path in files:
                    try:
                        with open(file_path, 'r') as in_file:
                            out_file.write(in_file.read())
                    except Exception as e:
                        print(f"Warning: Could not read {file_path}: {e}")
            print(f"Created {output_files[i]} with size {output_sizes[i]} bytes")
        except Exception as e:
            print(f"Error creating {output_files[i]}: {e}")

if __name__ == "__main__":
    merge_and_distribute_files()
## How the Script Works 1. Read a list of file paths from `/tmp/files` 2. For each path, get the file size 3. Sort files by size in descending order (largest first) 4. Use a min-heap to track the current size of each output file 5. Always add the next file to the smallest output file 6. Merge the content of each group into the corresponding output file This approach uses a greedy bin-packing algorithm that distributes files so that each of the 16 output files will have approximately the same total size.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import os
import heapq
import sys

def merge_and_distribute_files():
    # Read the list of file paths
    try:
        with open('/tmp/files', 'r') as f:
            file_paths = [line.strip() for line in f.readlines()]
    except Exception as e:
        print(f"Error reading input file: {e}")
        sys.exit(1)

    # Get file sizes and sort by size (largest first)
    file_sizes = []
    for file_path in file_paths:
        try:
            size = os.path.getsize(file_path)
            file_sizes.append((size, file_path))
        except Exception as e:
            print(f"Warning: Couldn't get size of {file_path}: {e}")
    
    if not file_sizes:
        print("No valid files found")
        return
        
    file_sizes.sort(reverse=True)
    
    # Initialize 16 output buckets
    output_files = [f'/tmp/merged_{i}' for i in range(16)]
    output_sizes = [0] * 16
    
    # Min heap to always get the smallest output file
    size_heap = [(0, i) for i in range(16)]
    heapq.heapify(size_heap)
    
    # Track which files go to which output
    file_distribution = [[] for _ in range(16)]
    
    # Distribute files to the output buckets
    for file_size, file_path in file_sizes:
        # Get the smallest output bucket
        current_size, index = heapq.heappop(size_heap)
        
        # Add file to this bucket
        file_distribution[index].append(file_path)
        
        # Update the size and put back in heap
        new_size = current_size + file_size
        output_sizes[index] = new_size
        heapq.heappush(size_heap, (new_size, index))
    
    # Now merge the files into each output file
    for i, files in enumerate(file_distribution):
        try:
            with open(output_files[i], 'w') as out_file:
                for file_path in files:
                    try:
                        with open(file_path, 'r') as in_file:
                            out_file.write(in_file.read())
                    except Exception as e:
                        print(f"Warning: Could not read {file_path}: {e}")
            print(f"Created {output_files[i]} with size {output_sizes[i]} bytes")
        except Exception as e:
            print(f"Error creating {output_files[i]}: {e}")

if __name__ == "__main__":
    merge_and_distribute_files()

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3
import os
import heapq
import sys

def merge_and_distribute_files():
    # Read the list of file paths
    try:
        with open('/tmp/files', 'r') as f:
            file_paths = [line.strip() for line in f.readlines()]
    except Exception as e:
        print(f"Error reading input file: {e}")
        sys.exit(1)

    # Get file sizes and sort by size (largest first)
    file_sizes = []
    for file_path in file_paths:
        try:
            size = os.path.getsize(file_path)
            file_sizes.append((size, file_path))
        except Exception as e:
            print(f"Warning: Couldn't get size of {file_path}: {e}")
    
    if not file_sizes:
        print("No valid files found")
        return
        
    file_sizes.sort(reverse=True)
    
    # Initialize 16 output buckets
    output_files = [f'/tmp/merged_{i}' for i in range(16)]
    output_sizes = [0] * 16
    
    # Min heap to always get the smallest output file
    size_heap = [(0, i) for i in range(16)]
    heapq.heapify(size_heap)
    
    # Track which files go to which output
    file_distribution = [[] for _ in range(16)]
    
    # Distribute files to the output buckets
    for file_size, file_path in file_sizes:
        # Get the smallest output bucket
        current_size, index = heapq.heappop(size_heap)
        
        # Add file to this bucket
        file_distribution[index].append(file_path)
        
        # Update the size and put back in heap
        new_size = current_size + file_size
        output_sizes[index] = new_size
        heapq.heappush(size_heap, (new_size, index))
    
    # Now merge the files into each output file
    for i, files in enumerate(file_distribution):
        try:
            with open(output_files[i], 'w') as out_file:
                for file_path in files:
                    try:
                        with open(file_path, 'r') as in_file:
                            out_file.write(in_file.read())
                    except Exception as e:
                        print(f"Warning: Could not read {file_path}: {e}")
            print(f"Created {output_files[i]} with size {output_sizes[i]} bytes")
        except Exception as e:
            print(f"Error creating {output_files[i]}: {e}")

if __name__ == "__main__":
    merge_and_distribute_files()
And got the output: Created /tmp/merged_0 with size 36998 bytes Created /tmp/merged_1 with size 37004 bytes Created /tmp/merged_2 with size 36999 bytes Created /tmp/merged_3 with size 37004 bytes Created /tmp/merged_4 with size 37003 bytes Created /tmp/merged_5 with size 37003 bytes Created /tmp/merged_6 with size 36997 bytes Created /tmp/merged_7 with size 36998 bytes Created /tmp/merged_8 with size 37002 bytes Created /tmp/merged_9 with size 36996 bytes Created /tmp/merged_10 with size 37002 bytes Created /tmp/merged_11 with size 37000 bytes Created /tmp/merged_12 with size 36999 bytes Created /tmp/merged_13 with size 36997 bytes Created /tmp/merged_14 with size 37004 bytes Created /tmp/merged_15 with size 36995 bytes

PyFunc

final: True Resulting in output: True