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

Here's a Python script that implements the file merging and balanced splitting logic you described: 

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import os
from heapq import heappush, heappop

# Read the list of file paths
with open('/tmp/files', 'r') as f:
    files = [line.strip() for line in f.readlines()]

# Get file sizes and sort by size (largest first)
file_sizes = [(os.path.getsize(f), f) for f in files if os.path.exists(f)]
file_sizes.sort(reverse=True)

# Initialize 16 output files with their current sizes
output_files = [(0, i) for i in range(16)]
file_contents = {i: [] for i in range(16)}

# Process each input file, always adding to the smallest output file
for size, filepath in file_sizes:
    # Get the smallest output file
    current_size, index = heappop(output_files)
    
    # Read the content of the input file
    try:
        with open(filepath, 'r') as f:
            content = f.read()
        
        # Add content to the selected output file's buffer
        file_contents[index].append(content)
        
        # Update the size and push back to heap
        heappush(output_files, (current_size + size, index))
    except Exception as e:
        print(f"Error processing {filepath}: {str(e)}")
        # Push back the original size if there was an error
        heappush(output_files, (current_size, index))

# Write all buffered content to the output files
for i in range(16):
    output_path = f'/tmp/merged_{i}'
    try:
        with open(output_path, 'w') as f:
            f.write('\n'.join(file_contents[i]))
    except Exception as e:
        print(f"Error writing to {output_path}: {str(e)}")

# Print final sizes for verification
final_sizes = [(os.path.getsize(f'/tmp/merged_{i}'), i) for i in range(16)]
final_sizes.sort(reverse=True)
print("\nFinal file sizes:")
for size, index in final_sizes:
    print(f"merged_{index}: {size:,} bytes")
This script: 1. Reads the list of file paths from `/tmp/files` 2. Gets the size of each file and sorts them by size (largest first) 3. Uses a min-heap to keep track of output file sizes, always selecting the smallest one to add to 4. Maintains content buffers in memory for each output file 5. Processes each input file in order of size (largest to smallest): - Selects the smallest output file - Reads the input file content - Adds it to the appropriate buffer - Updates the size tracking 6. Finally writes all buffered content to the 16 output files 7. Prints the final sizes of all output files for verification Features: - Error handling for file operations - Memory efficient by using buffers - Uses a min-heap for efficient "smallest file" selection - Handles the case where input files might be missing - Provides size verification output The result will be 16 files named `/tmp/merged_0` through `/tmp/merged_15`, each containing a portion of the merged content, with sizes as balanced as possible given the input file sizes. Note: If the input files are very large, you might want to modify the script to stream the content rather than loading it all into memory. Here's how you could modify the content handling part to be more memory efficient: 
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# Replace the content reading part with:
def append_file_to_output(input_path, output_path):
    with open(output_path, 'a') as out_f:
        with open(input_path, 'r') as in_f:
            for line in in_f:
                out_f.write(line)
            out_f.write('\n')

# And replace the content buffering with direct writing:
for size, filepath in file_sizes:
    current_size, index = heappop(output_files)
    output_path = f'/tmp/merged_{index}'
    
    try:
        append_file_to_output(filepath, output_path)
        heappush(output_files, (current_size + size, index))
    except Exception as e:
        print(f"Error processing {filepath}: {str(e)}")
        heappush(output_files, (current_size, index))
This streaming version will use much less memory but might be slightly slower due to more frequent disk I/O.

Extract Code

I extracted the following code from that output: 

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import os
from heapq import heappush, heappop

def main():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        files = [line.strip() for line in f.readlines()]

    # Get file sizes and sort by size (largest first)
    file_sizes = [(os.path.getsize(f), f) for f in files if os.path.exists(f)]
    file_sizes.sort(reverse=True)

    # Initialize 16 output files with their current sizes
    output_files = [(0, i) for i in range(16)]
    file_contents = {i: [] for i in range(16)}

    # Process each input file, always adding to the smallest output file
    for size, filepath in file_sizes:
        # Get the smallest output file
        current_size, index = heappop(output_files)
        
        # Read the content of the input file
        try:
            with open(filepath, 'r') as f:
                content = f.read()
            
            # Add content to the selected output file's buffer
            file_contents[index].append(content)
            
            # Update the size and push back to heap
            heappush(output_files, (current_size + size, index))
        except Exception as e:
            print(f"Error processing {filepath}: {str(e)}")
            # Push back the original size if there was an error
            heappush(output_files, (current_size, index))

    # Write all buffered content to the output files  
    for i in range(16):
        output_path = f'/tmp/merged_{i}'
        try:
            with open(output_path, 'w') as f:
                f.write('\n'.join(file_contents[i]))
        except Exception as e:
            print(f"Error writing to {output_path}: {str(e)}")

    # Print final sizes for verification
    final_sizes = [(os.path.getsize(f'/tmp/merged_{i}'), i) for i in range(16)]
    final_sizes.sort(reverse=True)
    print("\nFinal file sizes:")
    for size, index in final_sizes:
        print(f"merged_{index}: {size:,} bytes")

if __name__ == "__main__":
    main()

Run Code Interpreter

Running the following program: 

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import os
from heapq import heappush, heappop

def main():
    # Read the list of file paths
    with open('/tmp/files', 'r') as f:
        files = [line.strip() for line in f.readlines()]

    # Get file sizes and sort by size (largest first)
    file_sizes = [(os.path.getsize(f), f) for f in files if os.path.exists(f)]
    file_sizes.sort(reverse=True)

    # Initialize 16 output files with their current sizes
    output_files = [(0, i) for i in range(16)]
    file_contents = {i: [] for i in range(16)}

    # Process each input file, always adding to the smallest output file
    for size, filepath in file_sizes:
        # Get the smallest output file
        current_size, index = heappop(output_files)
        
        # Read the content of the input file
        try:
            with open(filepath, 'r') as f:
                content = f.read()
            
            # Add content to the selected output file's buffer
            file_contents[index].append(content)
            
            # Update the size and push back to heap
            heappush(output_files, (current_size + size, index))
        except Exception as e:
            print(f"Error processing {filepath}: {str(e)}")
            # Push back the original size if there was an error
            heappush(output_files, (current_size, index))

    # Write all buffered content to the output files  
    for i in range(16):
        output_path = f'/tmp/merged_{i}'
        try:
            with open(output_path, 'w') as f:
                f.write('\n'.join(file_contents[i]))
        except Exception as e:
            print(f"Error writing to {output_path}: {str(e)}")

    # Print final sizes for verification
    final_sizes = [(os.path.getsize(f'/tmp/merged_{i}'), i) for i in range(16)]
    final_sizes.sort(reverse=True)
    print("\nFinal file sizes:")
    for size, index in final_sizes:
        print(f"merged_{index}: {size:,} bytes")

if __name__ == "__main__":
    main()
And got the output: Final file sizes: merged_9: 36,701 bytes merged_7: 36,699 bytes merged_8: 36,698 bytes merged_5: 36,696 bytes merged_13: 36,692 bytes merged_11: 36,692 bytes merged_10: 36,692 bytes merged_15: 36,691 bytes merged_14: 36,691 bytes merged_6: 36,691 bytes merged_12: 36,690 bytes merged_3: 36,688 bytes merged_4: 36,684 bytes merged_2: 36,673 bytes merged_1: 36,669 bytes merged_0: 36,664 bytes

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

Here's a Python script that accomplishes this task: 

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import os
from heapq import heappush, heappop

def get_file_sizes(file_list_path):
    # Read file paths and get their sizes
    file_sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            path = line.strip()
            try:
                size = os.path.getsize(path)
                file_sizes.append((size, path))
            except OSError:
                print(f"Warning: Could not get size of {path}")
    
    # Sort by size (largest first)
    return sorted(file_sizes, reverse=True)

def merge_files():
    NUM_OUTPUT_FILES = 16
    output_files = [f"/tmp/merged_{i}" for i in range(NUM_OUTPUT_FILES)]
    
    # Initialize heap to track output file sizes
    # Each element is (total_size, output_file_index)
    size_heap = [(0, i) for i in range(NUM_OUTPUT_FILES)]
    
    # Get sorted list of (size, path) tuples
    file_sizes = get_file_sizes('/tmp/files')
    
    # Open all output files
    out_fps = [open(f, 'wb') for f in output_files]
    
    try:
        # Process each input file
        for file_size, file_path in file_sizes:
            # Get index of smallest output file
            current_size, smallest_idx = heappop(size_heap)
            
            try:
                # Read and write content
                with open(file_path, 'rb') as in_f:
                    content = in_f.read()
                    out_fps[smallest_idx].write(content)
                    
                    if len(content) > 0:
                        out_fps[smallest_idx].write(b'\n')
                
                # Update heap with new size
                heappush(size_heap, (current_size + file_size, smallest_idx))
                
            except IOError as e:
                print(f"Warning: Error processing {file_path}: {e}")
                # Put back the size/index without adding file size
                heappush(size_heap, (current_size, smallest_idx))
                
    finally:
        # Close all output files
        for fp in out_fps:
            fp.close()
    
    # Print final sizes for verification
    final_sizes = [(os.path.getsize(f"/tmp/merged_{i}"), i) for i in range(NUM_OUTPUT_FILES)]
    print("\nFinal file sizes:")
    for size, idx in sorted(final_sizes):
        print(f"/tmp/merged_{idx}: {size:,} bytes")

if __name__ == "__main__":
    merge_files()
This script: 1. Reads the list of file paths from `/tmp/files` 2. Gets the size of each file and sorts them in descending order 3. Creates 16 output files named `/tmp/merged_0` through `/tmp/merged_15` 4. Uses a min heap to track which output file is currently the smallest 5. For each input file (starting with largest): - Finds the output file with smallest current size - Appends the input file's contents to that output file - Updates the size tracking heap 6. Handles potential errors (missing files, permission issues, etc.) 7. Prints the final sizes of all output files for verification Key features: - Uses binary mode for file operations to handle any file type - Adds newlines between merged files - Error handling for missing or unreadable files - Memory efficient: reads and writes one file at a time - Uses a heap for efficient tracking of smallest output file - Prints final sizes to verify roughly equal distribution To use it, simply run the script after creating your `/tmp/files` list. The script will create the 16 output files with roughly equal sizes. Note: This assumes you have read permission for all input files and write permission for `/tmp/`.

Extract Code

I extracted the following code from that output: 

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import os
from heapq import heappush, heappop

def get_file_sizes(file_list_path):
    # Read file paths and get their sizes
    file_sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            path = line.strip()
            try:
                size = os.path.getsize(path)
                file_sizes.append((size, path))
            except OSError:
                print(f"Warning: Could not get size of {path}")
    
    # Sort by size (largest first)
    return sorted(file_sizes, reverse=True)

def merge_files():
    NUM_OUTPUT_FILES = 16
    output_files = [f"/tmp/merged_{i}" for i in range(NUM_OUTPUT_FILES)]
    
    # Initialize heap to track output file sizes
    # Each element is (total_size, output_file_index)
    size_heap = [(0, i) for i in range(NUM_OUTPUT_FILES)]
    
    # Get sorted list of (size, path) tuples
    file_sizes = get_file_sizes('/tmp/files')
    
    # Open all output files
    out_fps = [open(f, 'wb') for f in output_files]
    
    try:
        # Process each input file
        for file_size, file_path in file_sizes:
            # Get index of smallest output file
            current_size, smallest_idx = heappop(size_heap)
            
            try:
                # Read and write content
                with open(file_path, 'rb') as in_f:
                    content = in_f.read()
                    out_fps[smallest_idx].write(content)
                    
                    if len(content) > 0:
                        out_fps[smallest_idx].write(b'\n')
                
                # Update heap with new size
                heappush(size_heap, (current_size + file_size, smallest_idx))
                
            except IOError as e:
                print(f"Warning: Error processing {file_path}: {e}")
                # Put back the size/index without adding file size
                heappush(size_heap, (current_size, smallest_idx))
                
    finally:
        # Close all output files
        for fp in out_fps:
            fp.close()
    
    # Print final sizes for verification
    final_sizes = [(os.path.getsize(f"/tmp/merged_{i}"), i) for i in range(NUM_OUTPUT_FILES)]
    print("\nFinal file sizes:")
    for size, idx in sorted(final_sizes):
        print(f"/tmp/merged_{idx}: {size:,} bytes")

if __name__ == "__main__":
    merge_files()

Run Code Interpreter

Running the following program: 

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import os
from heapq import heappush, heappop

def get_file_sizes(file_list_path):
    # Read file paths and get their sizes
    file_sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            path = line.strip()
            try:
                size = os.path.getsize(path)
                file_sizes.append((size, path))
            except OSError:
                print(f"Warning: Could not get size of {path}")
    
    # Sort by size (largest first)
    return sorted(file_sizes, reverse=True)

def merge_files():
    NUM_OUTPUT_FILES = 16
    output_files = [f"/tmp/merged_{i}" for i in range(NUM_OUTPUT_FILES)]
    
    # Initialize heap to track output file sizes
    # Each element is (total_size, output_file_index)
    size_heap = [(0, i) for i in range(NUM_OUTPUT_FILES)]
    
    # Get sorted list of (size, path) tuples
    file_sizes = get_file_sizes('/tmp/files')
    
    # Open all output files
    out_fps = [open(f, 'wb') for f in output_files]
    
    try:
        # Process each input file
        for file_size, file_path in file_sizes:
            # Get index of smallest output file
            current_size, smallest_idx = heappop(size_heap)
            
            try:
                # Read and write content
                with open(file_path, 'rb') as in_f:
                    content = in_f.read()
                    out_fps[smallest_idx].write(content)
                    
                    if len(content) > 0:
                        out_fps[smallest_idx].write(b'\n')
                
                # Update heap with new size
                heappush(size_heap, (current_size + file_size, smallest_idx))
                
            except IOError as e:
                print(f"Warning: Error processing {file_path}: {e}")
                # Put back the size/index without adding file size
                heappush(size_heap, (current_size, smallest_idx))
                
    finally:
        # Close all output files
        for fp in out_fps:
            fp.close()
    
    # Print final sizes for verification
    final_sizes = [(os.path.getsize(f"/tmp/merged_{i}"), i) for i in range(NUM_OUTPUT_FILES)]
    print("\nFinal file sizes:")
    for size, idx in sorted(final_sizes):
        print(f"/tmp/merged_{idx}: {size:,} bytes")

if __name__ == "__main__":
    merge_files()
And got the output: Final file sizes: /tmp/merged_1: 38,638 bytes /tmp/merged_2: 38,639 bytes /tmp/merged_0: 38,640 bytes /tmp/merged_3: 38,650 bytes /tmp/merged_4: 38,660 bytes /tmp/merged_15: 38,667 bytes /tmp/merged_6: 38,668 bytes /tmp/merged_8: 38,668 bytes /tmp/merged_12: 38,668 bytes /tmp/merged_10: 38,669 bytes /tmp/merged_11: 38,669 bytes /tmp/merged_13: 38,669 bytes /tmp/merged_14: 38,669 bytes /tmp/merged_5: 38,670 bytes /tmp/merged_7: 38,670 bytes /tmp/merged_9: 38,670 bytes

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

Here's a Python script that implements the merge and split logic you described: 

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import os
from heapq import heappush, heappop

def get_file_sizes(file_list_path):
    # Read file paths and get their sizes
    files_with_sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            path = line.strip()
            try:
                size = os.path.getsize(path)
                # Store negative size for reverse sorting
                files_with_sizes.append((-size, path))
            except OSError:
                print(f"Warning: Could not access file {path}")
    return sorted(files_with_sizes)  # Sort by size (largest first due to negative size)

def merge_and_split(file_list_path, num_output_files=16):
    # Initialize output files with size 0
    output_files = [(0, i) for i in range(num_output_files)]
    output_handles = [open(f"/tmp/merged_{i}", 'wb') for i in range(num_output_files)]

    try:
        # Get sorted list of files by size
        files = get_file_sizes(file_list_path)

        # Process each file
        for negative_size, filepath in files:
            try:
                # Read input file
                with open(filepath, 'rb') as infile:
                    content = infile.read()

                # Find the smallest output file
                smallest_size, smallest_idx = output_files[0]
                
                # Write to the smallest file
                output_handles[smallest_idx].write(content)
                
                # Update size tracking
                new_size = smallest_size + len(content)
                output_files[0] = (new_size, smallest_idx)
                
                # Re-sort output files by size
                output_files.sort()

            except IOError as e:
                print(f"Error processing file {filepath}: {e}")

    finally:
        # Close all output files
        for handle in output_handles:
            handle.close()

    # Print final sizes
    for size, idx in sorted(output_files, key=lambda x: x[1]):
        print(f"Output file /tmp/merged_{idx}: {size} bytes")

if __name__ == "__main__":
    merge_and_split("/tmp/files")
This script: 1. Reads the list of file paths from `/tmp/files` 2. Gets the size of each file and sorts them by size (largest first) 3. Creates 16 output files named `/tmp/merged_0` through `/tmp/merged_15` 4. Maintains a list of output files sorted by their current size 5. For each input file: - Reads its content - Adds it to the smallest output file - Updates the size tracking - Re-sorts the output files by size 6. Always writes to the smallest output file, which helps maintain balance 7. Prints the final size of each output file Features: - Uses binary mode for file operations to handle any file type - Includes error handling for file access issues - Properly closes all file handles even if errors occur - Tracks and displays the final size of each output file To use it, simply run: 
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python script.py
The script assumes: - `/tmp/files` contains a list of valid file paths, one per line - The script has permission to read all input files - The script has permission to write to `/tmp/` - There's enough disk space for the merged files The output files will be created as `/tmp/merged_0` through `/tmp/merged_15`, each containing approximately equal amounts of data.

Extract Code

I extracted the following code from that output: 

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import os
from heapq import heappush, heappop

def get_file_sizes(file_list_path):
    # Read file paths and get their sizes
    files_with_sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            path = line.strip()
            try:
                size = os.path.getsize(path)
                # Store negative size for reverse sorting
                files_with_sizes.append((-size, path))
            except OSError:
                print(f"Warning: Could not access file {path}")
    return sorted(files_with_sizes)  # Sort by size (largest first due to negative size)

def merge_and_split(file_list_path, num_output_files=16):
    # Initialize output files with size 0
    output_files = [(0, i) for i in range(num_output_files)]
    output_handles = [open(f"/tmp/merged_{i}", 'wb') for i in range(num_output_files)]

    try:
        # Get sorted list of files by size
        files = get_file_sizes(file_list_path)

        # Process each file
        for negative_size, filepath in files:
            try:
                # Read input file
                with open(filepath, 'rb') as infile:
                    content = infile.read()

                # Find the smallest output file
                smallest_size, smallest_idx = output_files[0]
                
                # Write to the smallest file
                output_handles[smallest_idx].write(content)
                
                # Update size tracking
                new_size = smallest_size + len(content)
                output_files[0] = (new_size, smallest_idx)
                
                # Re-sort output files by size
                output_files.sort()

            except IOError as e:
                print(f"Error processing file {filepath}: {e}")

    finally:
        # Close all output files
        for handle in output_handles:
            handle.close()

    # Print final sizes
    for size, idx in sorted(output_files, key=lambda x: x[1]):
        print(f"Output file /tmp/merged_{idx}: {size} bytes")

if __name__ == "__main__":
    merge_and_split("/tmp/files")

Run Code Interpreter

Running the following program: 

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import os
from heapq import heappush, heappop

def get_file_sizes(file_list_path):
    # Read file paths and get their sizes
    files_with_sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            path = line.strip()
            try:
                size = os.path.getsize(path)
                # Store negative size for reverse sorting
                files_with_sizes.append((-size, path))
            except OSError:
                print(f"Warning: Could not access file {path}")
    return sorted(files_with_sizes)  # Sort by size (largest first due to negative size)

def merge_and_split(file_list_path, num_output_files=16):
    # Initialize output files with size 0
    output_files = [(0, i) for i in range(num_output_files)]
    output_handles = [open(f"/tmp/merged_{i}", 'wb') for i in range(num_output_files)]

    try:
        # Get sorted list of files by size
        files = get_file_sizes(file_list_path)

        # Process each file
        for negative_size, filepath in files:
            try:
                # Read input file
                with open(filepath, 'rb') as infile:
                    content = infile.read()

                # Find the smallest output file
                smallest_size, smallest_idx = output_files[0]
                
                # Write to the smallest file
                output_handles[smallest_idx].write(content)
                
                # Update size tracking
                new_size = smallest_size + len(content)
                output_files[0] = (new_size, smallest_idx)
                
                # Re-sort output files by size
                output_files.sort()

            except IOError as e:
                print(f"Error processing file {filepath}: {e}")

    finally:
        # Close all output files
        for handle in output_handles:
            handle.close()

    # Print final sizes
    for size, idx in sorted(output_files, key=lambda x: x[1]):
        print(f"Output file /tmp/merged_{idx}: {size} bytes")

if __name__ == "__main__":
    merge_and_split("/tmp/files")
And got the output: Output file /tmp/merged_0: 37005 bytes Output file /tmp/merged_1: 37005 bytes Output file /tmp/merged_2: 37005 bytes Output file /tmp/merged_3: 37010 bytes Output file /tmp/merged_4: 37006 bytes Output file /tmp/merged_5: 37004 bytes Output file /tmp/merged_6: 37004 bytes Output file /tmp/merged_7: 37004 bytes Output file /tmp/merged_8: 37004 bytes Output file /tmp/merged_9: 37006 bytes Output file /tmp/merged_10: 37005 bytes Output file /tmp/merged_11: 37005 bytes Output file /tmp/merged_12: 37005 bytes Output file /tmp/merged_13: 37006 bytes Output file /tmp/merged_14: 37003 bytes Output file /tmp/merged_15: 37004 bytes

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

Here's a Python script that implements this file merging and balancing logic: 

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import os
from heapq import heappush, heappop

def get_file_size(filepath):
    try:
        return os.path.getsize(filepath)
    except (OSError, IOError):
        return 0

# Read the list of files and their sizes
files_with_sizes = []
with open('/tmp/files', 'r') as f:
    for line in f:
        filepath = line.strip()
        size = get_file_size(filepath)
        files_with_sizes.append((size, filepath))

# Sort files by size in descending order
files_with_sizes.sort(reverse=True)

# Initialize output files with size tracking
output_files = [(0, i, open(f'/tmp/merged_{i}', 'wb')) for i in range(16)]

# Process each input file
for size, filepath in files_with_sizes:
    try:
        # Get the smallest output file (using heap)
        current_size, idx, outfile = min(output_files, key=lambda x: x[0])
        
        # Read and write the content
        try:
            with open(filepath, 'rb') as infile:
                content = infile.read()
                outfile.write(content)
                outfile.flush()
                
                # Update the size tracking
                output_files[idx] = (current_size + size, idx, outfile)
                
        except (IOError, OSError) as e:
            print(f"Error processing file {filepath}: {e}")
            continue
            
    except Exception as e:
        print(f"Unexpected error processing {filepath}: {e}")
        continue

# Close all output files
for _, _, f in output_files:
    try:
        f.close()
    except:
        pass

# Print final sizes for verification
final_sizes = []
for i in range(16):
    size = os.path.getsize(f'/tmp/merged_{i}')
    final_sizes.append(size)
    print(f"merged_{i}: {size:,} bytes")

print(f"Max size difference: {max(final_sizes) - min(final_sizes):,} bytes")
This script: 1. Reads the list of file paths from `/tmp/files` 2. Gets the size of each file and creates a list of (size, filepath) tuples 3. Sorts the files by size in descending order (largest first) 4. Creates 16 output files and tracks their sizes 5. For each input file: - Finds the output file with the smallest current size - Reads the input file content - Appends it to the selected output file - Updates the size tracking 6. Closes all files and prints size statistics Key features: - Uses binary mode (`'rb'` and `'wb'`) to handle all file types - Includes error handling for file operations - Tracks and reports final sizes - Processes files in size order (largest first) for better balancing - Uses minimal memory by streaming content rather than loading everything at once The script prints the final size of each output file and the maximum size difference between any two output files, which helps verify the balance. To use the script: 1. Make sure you have write permissions in `/tmp/` 2. Make sure `/tmp/files` exists with your list of file paths 3. Run the script The result will be 16 files named `/tmp/merged_0` through `/tmp/merged_15`, each containing approximately equal amounts of data.

Extract Code

I extracted the following code from that output: 

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import os
from heapq import heappush, heappop

def get_file_size(filepath):
    try:
        return os.path.getsize(filepath)
    except (OSError, IOError):
        return 0

# Read the list of files and their sizes
files_with_sizes = []
with open('/tmp/files', 'r') as f:
    for line in f:
        filepath = line.strip()
        size = get_file_size(filepath)
        files_with_sizes.append((size, filepath))

# Sort files by size in descending order
files_with_sizes.sort(reverse=True)

# Initialize output files with size tracking
output_files = [(0, i, open(f'/tmp/merged_{i}', 'wb')) for i in range(16)]

# Process each input file
for size, filepath in files_with_sizes:
    try:
        # Get the smallest output file (using heap)
        current_size, idx, outfile = min(output_files, key=lambda x: x[0])
        
        # Read and write the content
        try:
            with open(filepath, 'rb') as infile:
                content = infile.read()
                outfile.write(content)
                outfile.flush()
                
                # Update the size tracking
                output_files[idx] = (current_size + size, idx, outfile)
                
        except (IOError, OSError) as e:
            print(f"Error processing file {filepath}: {e}")
            continue
            
    except Exception as e:
        print(f"Unexpected error processing {filepath}: {e}")
        continue

# Close all output files
for _, _, f in output_files:
    try:
        f.close()
    except:
        pass

# Print final sizes for verification
final_sizes = []
for i in range(16):
    size = os.path.getsize(f'/tmp/merged_{i}')
    final_sizes.append(size)
    print(f"merged_{i}: {size:,} bytes")

print(f"Max size difference: {max(final_sizes) - min(final_sizes):,} bytes")

Run Code Interpreter

Running the following program: 

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import os
from heapq import heappush, heappop

def get_file_size(filepath):
    try:
        return os.path.getsize(filepath)
    except (OSError, IOError):
        return 0

# Read the list of files and their sizes
files_with_sizes = []
with open('/tmp/files', 'r') as f:
    for line in f:
        filepath = line.strip()
        size = get_file_size(filepath)
        files_with_sizes.append((size, filepath))

# Sort files by size in descending order
files_with_sizes.sort(reverse=True)

# Initialize output files with size tracking
output_files = [(0, i, open(f'/tmp/merged_{i}', 'wb')) for i in range(16)]

# Process each input file
for size, filepath in files_with_sizes:
    try:
        # Get the smallest output file (using heap)
        current_size, idx, outfile = min(output_files, key=lambda x: x[0])
        
        # Read and write the content
        try:
            with open(filepath, 'rb') as infile:
                content = infile.read()
                outfile.write(content)
                outfile.flush()
                
                # Update the size tracking
                output_files[idx] = (current_size + size, idx, outfile)
                
        except (IOError, OSError) as e:
            print(f"Error processing file {filepath}: {e}")
            continue
            
    except Exception as e:
        print(f"Unexpected error processing {filepath}: {e}")
        continue

# Close all output files
for _, _, f in output_files:
    try:
        f.close()
    except:
        pass

# Print final sizes for verification
final_sizes = []
for i in range(16):
    size = os.path.getsize(f'/tmp/merged_{i}')
    final_sizes.append(size)
    print(f"merged_{i}: {size:,} bytes")

print(f"Max size difference: {max(final_sizes) - min(final_sizes):,} bytes")
And got the output: merged_0: 37,872 bytes merged_1: 37,871 bytes merged_2: 37,872 bytes merged_3: 37,872 bytes merged_4: 37,873 bytes merged_5: 37,872 bytes merged_6: 37,872 bytes merged_7: 37,871 bytes merged_8: 37,873 bytes merged_9: 37,870 bytes merged_10: 37,872 bytes merged_11: 37,873 bytes merged_12: 37,870 bytes merged_13: 37,868 bytes merged_14: 37,873 bytes merged_15: 37,868 bytes Max size difference: 5 bytes

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

Here's a Python script that implements the merging logic you described: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16
INPUT_LIST = '/tmp/files'
OUTPUT_PREFIX = '/tmp/merged_'

def get_file_size(filepath):
    try:
        return os.path.getsize(filepath)
    except (OSError, FileNotFoundError):
        return 0

# Read input file paths and sort by size
with open(INPUT_LIST, 'r') as f:
    files = [line.strip() for line in f if line.strip()]

# Get sizes and sort by size (largest first)
file_sizes = [(f, get_file_size(f)) for f in files]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize output files with size tracking
output_files = [{'path': f'{OUTPUT_PREFIX}{i}', 'size': 0} for i in range(NUM_OUTPUT_FILES)]

# Process each input file
for filepath, size in file_sizes:
    try:
        # Find the smallest output file
        smallest_output = min(output_files, key=lambda x: x['size'])
        
        # Read input file content
        with open(filepath, 'rb') as infile:
            content = infile.read()
            
        # Append to the smallest output file
        with open(smallest_output['path'], 'ab') as outfile:
            outfile.write(content)
        
        # Update size tracking
        smallest_output['size'] += size
        
    except (OSError, FileNotFoundError) as e:
        print(f"Error processing {filepath}: {e}")

# Print final size distribution
total_size = sum(f['size'] for f in output_files)
print("\nFinal size distribution:")
for f in output_files:
    print(f"{f['path']}: {f['size']} bytes ({(f['size']/total_size)*100:.1f}%)")
This script: 1. Takes a list of file paths from `/tmp/files` 2. Gets the size of each file and sorts them in descending order 3. Creates 16 output files 4. Processes each input file in order (largest to smallest) 5. Always appends to the output file that currently has the smallest size 6. Prints the final size distribution Key features: - Uses binary mode for file operations to handle any file type - Error handling for missing or unreadable files - Tracks and reports size distribution - Memory efficient: reads and writes one file at a time - Preserves original file content exactly Usage: 
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python3 merge_files.py
Notes: - Make sure you have write permissions in /tmp/ - The script assumes enough disk space is available - Very large files might need additional memory management - The script will overwrite existing merged_* files if they exist You can modify NUM_OUTPUT_FILES and OUTPUT_PREFIX if needed. The script will create files named /tmp/merged_0 through /tmp/merged_15. To handle very large files or lots of files more efficiently, you could add buffered reading/writing or multiprocessing, but this basic version should work well for most cases.

Extract Code

I extracted the following code from that output: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16
INPUT_LIST = '/tmp/files'
OUTPUT_PREFIX = '/tmp/merged_'

def get_file_size(filepath):
    try:
        return os.path.getsize(filepath)
    except (OSError, FileNotFoundError):
        return 0

# Read input file paths and sort by size
with open(INPUT_LIST, 'r') as f:
    files = [line.strip() for line in f if line.strip()]

# Get sizes and sort by size (largest first)
file_sizes = [(f, get_file_size(f)) for f in files]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize output files with size tracking
output_files = [{'path': f'{OUTPUT_PREFIX}{i}', 'size': 0} for i in range(NUM_OUTPUT_FILES)]

# Process each input file
for filepath, size in file_sizes:
    try:
        # Find the smallest output file
        smallest_output = min(output_files, key=lambda x: x['size'])
        
        # Read input file content
        with open(filepath, 'rb') as infile:
            content = infile.read()
            
        # Append to the smallest output file
        with open(smallest_output['path'], 'ab') as outfile:
            outfile.write(content)
        
        # Update size tracking
        smallest_output['size'] += size
        
    except (OSError, FileNotFoundError) as e:
        print(f"Error processing {filepath}: {e}")

# Print final size distribution
total_size = sum(f['size'] for f in output_files)
print("\nFinal size distribution:")
for f in output_files:
    print(f"{f['path']}: {f['size']} bytes ({(f['size']/total_size)*100:.1f}%)")

if __name__ == '__main__':
    pass

Run Code Interpreter

Running the following program: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16
INPUT_LIST = '/tmp/files'
OUTPUT_PREFIX = '/tmp/merged_'

def get_file_size(filepath):
    try:
        return os.path.getsize(filepath)
    except (OSError, FileNotFoundError):
        return 0

# Read input file paths and sort by size
with open(INPUT_LIST, 'r') as f:
    files = [line.strip() for line in f if line.strip()]

# Get sizes and sort by size (largest first)
file_sizes = [(f, get_file_size(f)) for f in files]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize output files with size tracking
output_files = [{'path': f'{OUTPUT_PREFIX}{i}', 'size': 0} for i in range(NUM_OUTPUT_FILES)]

# Process each input file
for filepath, size in file_sizes:
    try:
        # Find the smallest output file
        smallest_output = min(output_files, key=lambda x: x['size'])
        
        # Read input file content
        with open(filepath, 'rb') as infile:
            content = infile.read()
            
        # Append to the smallest output file
        with open(smallest_output['path'], 'ab') as outfile:
            outfile.write(content)
        
        # Update size tracking
        smallest_output['size'] += size
        
    except (OSError, FileNotFoundError) as e:
        print(f"Error processing {filepath}: {e}")

# Print final size distribution
total_size = sum(f['size'] for f in output_files)
print("\nFinal size distribution:")
for f in output_files:
    print(f"{f['path']}: {f['size']} bytes ({(f['size']/total_size)*100:.1f}%)")

if __name__ == '__main__':
    pass
And got the output: Final size distribution: /tmp/merged_0: 37658 bytes (6.3%) /tmp/merged_1: 37655 bytes (6.2%) /tmp/merged_2: 37655 bytes (6.2%) /tmp/merged_3: 37656 bytes (6.3%) /tmp/merged_4: 37655 bytes (6.2%) /tmp/merged_5: 37656 bytes (6.3%) /tmp/merged_6: 37656 bytes (6.3%) /tmp/merged_7: 37653 bytes (6.2%) /tmp/merged_8: 37659 bytes (6.3%) /tmp/merged_9: 37657 bytes (6.3%) /tmp/merged_10: 37656 bytes (6.3%) /tmp/merged_11: 37654 bytes (6.2%) /tmp/merged_12: 37656 bytes (6.3%) /tmp/merged_13: 37658 bytes (6.3%) /tmp/merged_14: 37656 bytes (6.3%) /tmp/merged_15: 37655 bytes (6.2%)

PyFunc

final: True Resulting in output: True