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 what you've described: 

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import os
import heapq

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

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

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Write the content of the current file to the output file
    with open(path, 'rb') as input_file:
        output_files[index].write(input_file.read())
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for file in output_files:
    file.close()

print("Merging complete.")
This script does the following: 1. It reads the list of file paths from `/tmp/files`. 2. It gets the size of each file and sorts the list by size in descending order. 3. It initializes 16 output files named `/tmp/merged_0` through `/tmp/merged_15`. 4. It uses a min heap to keep track of the current sizes of the output files. This allows us to efficiently find the smallest file each time. 5. For each input file (starting with the largest): - It selects the smallest output file. - It reads the content of the input file and writes it to the selected output file. - It updates the size of the output file in the heap. 6. Finally, it closes all the output files. This approach ensures that files are distributed to keep the output files as balanced as possible in terms of size. The largest files are processed first, which helps in achieving a more even distribution. Note: This script assumes that you have permission to read all the input files and write to the `/tmp` directory. Also, it loads entire files into memory, which might not be suitable for extremely large files. If you need to handle very large files, you may want to modify the script to read and write in chunks.

Extract Code

I extracted the following code from that output: 

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import os
import heapq

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

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

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Write the content of the current file to the output file
    with open(path, 'rb') as input_file:
        output_files[index].write(input_file.read())
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for file in output_files:
    file.close()

print("Merging complete.")

Run Code Interpreter

Running the following program: 

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import os
import heapq

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

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

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Write the content of the current file to the output file
    with open(path, 'rb') as input_file:
        output_files[index].write(input_file.read())
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for file in output_files:
    file.close()

print("Merging complete.")
And got the output: Merging complete.

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 what you've described: 

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import os
import heapq

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

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

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

# Initialize a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read the content of the current file and write to the output file
    with open(path, 'r') as input_file:
        content = input_file.read()
        output_files[index].write(content)
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for file in output_files:
    file.close()

print("Merging and splitting complete.")
This script does the following: 1. It reads the list of file paths from `/tmp/files`. 2. It gets the size of each file and sorts the list by size in descending order. 3. It initializes 16 output files named `/tmp/merged_0` through `/tmp/merged_15`. 4. It uses a min heap to keep track of the current sizes of the output files. This allows us to efficiently find the smallest file each time. 5. For each input file (starting with the largest): - It selects the smallest output file. - It reads the content of the input file and appends it to the selected output file. - It updates the size of the output file in the heap. 6. Finally, it closes all the output files. This approach ensures that files are distributed to always add to the smallest output file, which should result in output files of approximately equal size. Note: This script assumes that all input files are text files. If you're dealing with binary files, you'll need to open them in binary mode ('rb' and 'wb') instead. Also, be aware that this script loads each input file entirely into memory. If you're dealing with very large files, you might need to modify the script to read and write in chunks instead.

Extract Code

I extracted the following code from that output: 

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import os
import heapq

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

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

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

# Initialize a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read the content of the current file and write to the output file
    with open(path, 'r') as input_file:
        content = input_file.read()
        output_files[index].write(content)
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for file in output_files:
    file.close()

print("Merging and splitting complete.")

Run Code Interpreter

Running the following program: 

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import os
import heapq

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

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

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

# Initialize a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read the content of the current file and write to the output file
    with open(path, 'r') as input_file:
        content = input_file.read()
        output_files[index].write(content)
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for file in output_files:
    file.close()

print("Merging and splitting complete.")
And got the output: Merging and splitting complete.

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 what you've described: 

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import os
import heapq

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

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

# Sort files by size (largest first)
file_sizes = [(get_file_size(path), path) for path in file_paths]
file_sizes.sort(reverse=True)

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read the content of the file and write it to the output file
    try:
        with open(path, 'r') as input_file:
            content = input_file.read()
            output_files[index].write(content)
            if content and not content.endswith('\n'):
                output_files[index].write('\n')
    except IOError:
        print(f"Error reading file: {path}")
        continue
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for f in output_files:
    f.close()

print("Merging complete!")
This script does the following: 1. It defines a helper function `get_file_size` to safely get the size of a file. 2. It reads the list of file paths from `/tmp/files`. 3. It sorts the files by size in descending order. 4. It initializes 16 output files named `/tmp/merged_0` to `/tmp/merged_15`. 5. It uses a min heap to keep track of the current sizes of the output files, always allowing us to quickly find the smallest one. 6. It iterates through the sorted list of files: - Pops the smallest output file from the heap - Reads the content of the current input file - Writes the content to the selected output file - Updates the size of the output file and pushes it back to the heap 7. Finally, it closes all the output files. This approach ensures that larger files are distributed first, and each new file is always added to the currently smallest output file, which helps maintain balance among the output files. Note: This script assumes that all input files are text files. If you're dealing with binary files or very large files, you might need to modify the file reading and writing parts accordingly. Also, be aware that this script loads entire files into memory. If you're dealing with very large files, you might want to consider reading and writing in chunks to reduce memory usage.

Extract Code

I extracted the following code from that output: 

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import os
import heapq

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

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

# Sort files by size (largest first)
file_sizes = [(get_file_size(path), path) for path in file_paths]
file_sizes.sort(reverse=True)

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read the content of the file and write it to the output file
    try:
        with open(path, 'r') as input_file:
            content = input_file.read()
            output_files[index].write(content)
            if content and not content.endswith('\n'):
                output_files[index].write('\n')
    except IOError:
        print(f"Error reading file: {path}")
        continue
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for f in output_files:
    f.close()

print("Merging complete!")

Run Code Interpreter

Running the following program: 

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import os
import heapq

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

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

# Sort files by size (largest first)
file_sizes = [(get_file_size(path), path) for path in file_paths]
file_sizes.sort(reverse=True)

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, path in file_sizes:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read the content of the file and write it to the output file
    try:
        with open(path, 'r') as input_file:
            content = input_file.read()
            output_files[index].write(content)
            if content and not content.endswith('\n'):
                output_files[index].write('\n')
    except IOError:
        print(f"Error reading file: {path}")
        continue
    
    # Update the size and push back to the heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for f in output_files:
    f.close()

print("Merging complete!")
And got the output: Merging complete!

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 the task you've described: 

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import os
import heapq

def get_file_size(file_path):
    try:
        return os.path.getsize(file_path)
    except OSError:
        return 0

def merge_and_split_files(input_file, output_prefix, num_outputs=16):
    # Read file paths from input file
    with open(input_file, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size (largest first)
    sorted_files = sorted(file_paths, key=get_file_size, reverse=True)

    # Initialize output files
    output_files = [open(f"{output_prefix}_{i}", 'wb') for i in range(num_outputs)]
    file_sizes = [0] * num_outputs

    # Use a min heap to keep track of the smallest file
    heap = [(0, i) for i in range(num_outputs)]
    heapq.heapify(heap)

    # Process each file
    for file_path in sorted_files:
        try:
            with open(file_path, 'rb') as f:
                content = f.read()
            
            # Get the index of the smallest output file
            size, index = heapq.heappop(heap)
            
            # Write content to the smallest output file
            output_files[index].write(content)
            
            # Update file size and push back to heap
            new_size = size + len(content)
            file_sizes[index] = new_size
            heapq.heappush(heap, (new_size, index))
        
        except IOError:
            print(f"Error reading file: {file_path}")

    # Close all output files
    for f in output_files:
        f.close()

    # Print file sizes for verification
    for i, size in enumerate(file_sizes):
        print(f"Output file {i}: {size} bytes")

# Run the script
merge_and_split_files('/tmp/files', '/tmp/merged', 16)
This script does the following: 1. It defines a function `get_file_size` to safely get the size of a file. 2. The main function `merge_and_split_files` does the following: - Reads the file paths from the input file. - Sorts the files by size (largest first). - Initializes 16 output files. - Uses a min heap to keep track of which output file is currently the smallest. - Processes each input file: - Reads its content. - Writes the content to the smallest output file. - Updates the size of that output file in the heap. 3. It handles potential errors when reading files. 4. After processing all files, it closes the output files and prints the sizes of each output file for verification. 5. Finally, it calls the function with the specified parameters. This script ensures that the files are distributed among the output files in a way that keeps their sizes as balanced as possible, by always adding to the smallest output file. The use of a min heap makes this process efficient. To run this script, save it to a file (e.g., `merge_split.py`) and run it using Python: 
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python merge_split.py
Make sure you have the necessary permissions to read from `/tmp/files` and write to `/tmp/merged_*`.

Extract Code

I extracted the following code from that output: 

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import os
import heapq

def get_file_size(file_path):
    try:
        return os.path.getsize(file_path)
    except OSError:
        return 0

def merge_and_split_files(input_file, output_prefix, num_outputs=16):
    # Read file paths from input file
    with open(input_file, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size (largest first)
    sorted_files = sorted(file_paths, key=get_file_size, reverse=True)

    # Initialize output files
    output_files = [open(f"{output_prefix}_{i}", 'wb') for i in range(num_outputs)]
    file_sizes = [0] * num_outputs

    # Use a min heap to keep track of the smallest file
    heap = [(0, i) for i in range(num_outputs)]
    heapq.heapify(heap)

    # Process each file
    for file_path in sorted_files:
        try:
            with open(file_path, 'rb') as f:
                content = f.read()
            
            # Get the index of the smallest output file
            size, index = heapq.heappop(heap)
            
            # Write content to the smallest output file
            output_files[index].write(content)
            
            # Update file size and push back to heap
            new_size = size + len(content)
            file_sizes[index] = new_size
            heapq.heappush(heap, (new_size, index))
        
        except IOError:
            print(f"Error reading file: {file_path}")

    # Close all output files
    for f in output_files:
        f.close()

    # Print file sizes for verification
    for i, size in enumerate(file_sizes):
        print(f"Output file {i}: {size} bytes")

if __name__ == "__main__":
    merge_and_split_files('/tmp/files', '/tmp/merged', 16)

Run Code Interpreter

Running the following program: 

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import os
import heapq

def get_file_size(file_path):
    try:
        return os.path.getsize(file_path)
    except OSError:
        return 0

def merge_and_split_files(input_file, output_prefix, num_outputs=16):
    # Read file paths from input file
    with open(input_file, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size (largest first)
    sorted_files = sorted(file_paths, key=get_file_size, reverse=True)

    # Initialize output files
    output_files = [open(f"{output_prefix}_{i}", 'wb') for i in range(num_outputs)]
    file_sizes = [0] * num_outputs

    # Use a min heap to keep track of the smallest file
    heap = [(0, i) for i in range(num_outputs)]
    heapq.heapify(heap)

    # Process each file
    for file_path in sorted_files:
        try:
            with open(file_path, 'rb') as f:
                content = f.read()
            
            # Get the index of the smallest output file
            size, index = heapq.heappop(heap)
            
            # Write content to the smallest output file
            output_files[index].write(content)
            
            # Update file size and push back to heap
            new_size = size + len(content)
            file_sizes[index] = new_size
            heapq.heappush(heap, (new_size, index))
        
        except IOError:
            print(f"Error reading file: {file_path}")

    # Close all output files
    for f in output_files:
        f.close()

    # Print file sizes for verification
    for i, size in enumerate(file_sizes):
        print(f"Output file {i}: {size} bytes")

if __name__ == "__main__":
    merge_and_split_files('/tmp/files', '/tmp/merged', 16)
And got the output: Output file 0: 36256 bytes Output file 1: 36255 bytes Output file 2: 36254 bytes Output file 3: 36254 bytes Output file 4: 36253 bytes Output file 5: 36252 bytes Output file 6: 36252 bytes Output file 7: 36253 bytes Output file 8: 36252 bytes Output file 9: 36255 bytes Output file 10: 36253 bytes Output file 11: 36252 bytes Output file 12: 36252 bytes Output file 13: 36251 bytes Output file 14: 36251 bytes Output file 15: 36252 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 what you've described: 

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import os
import heapq

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

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

# Sort files by size (largest first)
sorted_files = sorted([(get_file_size(fp), fp) for fp in filepaths], reverse=True)

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, filepath in sorted_files:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read and write the content
    try:
        with open(filepath, 'r') as input_file:
            content = input_file.read()
            output_files[index].write(content)
            if not content.endswith('\n'):
                output_files[index].write('\n')
    except IOError:
        print(f"Warning: Could not read file {filepath}")
    
    # Update the size and push back to heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for f in output_files:
    f.close()

print("Merging complete!")
This script does the following: 1. It defines a helper function `get_file_size` to safely get the size of each file. 2. It reads the list of file paths from `/tmp/files`. 3. It sorts the files by size in descending order. 4. It initializes 16 output files named `/tmp/merged_0` through `/tmp/merged_15`. 5. It uses a min heap to keep track of the current sizes of the output files, always allowing us to quickly find the smallest one. 6. It iterates through the sorted list of files: - Pops the smallest output file from the heap - Reads the content of the current input file - Writes the content to the selected output file - Updates the size of the output file and pushes it back to the heap 7. Finally, it closes all output files. This approach ensures that files are distributed to keep the output files as balanced as possible in terms of size. The largest files are processed first, which helps in achieving a more even distribution. Note: This script assumes that the files are text files. If you're dealing with binary files, you'll need to open them in binary mode ('rb' for reading and 'wb' for writing) and adjust the writing logic accordingly. Also, be aware that this script loads entire files into memory. If you're dealing with very large files, you might want to consider reading and writing in chunks to reduce memory usage.

Extract Code

I extracted the following code from that output: 

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import os
import heapq

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

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

# Sort files by size (largest first)
sorted_files = sorted([(get_file_size(fp), fp) for fp in filepaths], reverse=True)

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, filepath in sorted_files:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read and write the content
    try:
        with open(filepath, 'r') as input_file:
            content = input_file.read()
            output_files[index].write(content)
            if not content.endswith('\n'):
                output_files[index].write('\n')
    except IOError:
        print(f"Warning: Could not read file {filepath}")
    
    # Update the size and push back to heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for f in output_files:
    f.close()

print("Merging complete!")

Run Code Interpreter

Running the following program: 

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import os
import heapq

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

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

# Sort files by size (largest first)
sorted_files = sorted([(get_file_size(fp), fp) for fp in filepaths], reverse=True)

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

# Use a min heap to keep track of file sizes
heap = [(0, i) for i in range(16)]
heapq.heapify(heap)

# Distribute files
for size, filepath in sorted_files:
    # Get the smallest output file
    current_size, index = heapq.heappop(heap)
    
    # Read and write the content
    try:
        with open(filepath, 'r') as input_file:
            content = input_file.read()
            output_files[index].write(content)
            if not content.endswith('\n'):
                output_files[index].write('\n')
    except IOError:
        print(f"Warning: Could not read file {filepath}")
    
    # Update the size and push back to heap
    new_size = current_size + size
    heapq.heappush(heap, (new_size, index))

# Close all output files
for f in output_files:
    f.close()

print("Merging complete!")
And got the output: Merging complete!

PyFunc

final: True Resulting in output: True