Docker Setup

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

Initial Query

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

LLM Generation

Query

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

Output

Here's a Python script that will merge the contents of files listed in /tmp/files into 16 approximately equal-sized output files: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

# 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 total size of all files
total_size = 0
file_sizes = []
for path in file_paths:
    try:
        size = os.path.getsize(path)
        total_size += size
        file_sizes.append((path, size))
    except OSError:
        print(f"Warning: Could not access file {path}")
        continue

# Calculate target size per output file
target_size = total_size / NUM_OUTPUT_FILES

# Distribute files across output files
current_output = 0
current_size = 0

output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(NUM_OUTPUT_FILES)]

try:
    for filepath, size in file_sizes:
        try:
            with open(filepath, 'rb') as infile:
                data = infile.read()
                output_files[current_output].write(data)
                current_size += size
                
                # If current output file is approximately at target size, move to next
                if current_size >= target_size and current_output < NUM_OUTPUT_FILES - 1:
                    current_output += 1
                    current_size = 0
                    
        except IOError:
            print(f"Warning: Could not read file {filepath}")
            continue

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

print(f"Merged files into {NUM_OUTPUT_FILES} files of approximately {target_size/1024/1024:.2f} MB each")
This script: 1. Reads the list of file paths from /tmp/files 2. Calculates the total size of all input files 3. Determines the target size for each output file 4. Creates 16 output files 5. Reads each input file and writes its contents to the current output file 6. Moves to the next output file when the current one reaches approximately the target size 7. Handles errors for inaccessible files 8. Uses binary mode for reading/writing to handle all file types 9. Properly closes all files using a try/finally block The script tries to create output files of roughly equal size by tracking the accumulated size and switching to the next output file when reaching the target size. Note that: - The script handles binary files (uses 'rb' and 'wb' modes) - It includes basic error handling for inaccessible files - The final sizes might not be exactly equal due to varying file sizes - The script keeps all output files open simultaneously, which is generally fine for 16 files - If you're dealing with very large files, you might want to add buffer size controls You can run this script directly after saving it, and it will create the 16 merged files in /tmp/.

Extract Code

I extracted the following code from that output: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

# 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 total size of all files
total_size = 0
file_sizes = []
for path in file_paths:
    try:
        size = os.path.getsize(path)
        total_size += size
        file_sizes.append((path, size))
    except OSError:
        print(f"Warning: Could not access file {path}")
        continue

# Calculate target size per output file
target_size = total_size / NUM_OUTPUT_FILES

# Distribute files across output files
current_output = 0
current_size = 0

output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(NUM_OUTPUT_FILES)]

try:
    for filepath, size in file_sizes:
        try:
            with open(filepath, 'rb') as infile:
                data = infile.read()
                output_files[current_output].write(data)
                current_size += size
                
                # If current output file is approximately at target size, move to next
                if current_size >= target_size and current_output < NUM_OUTPUT_FILES - 1:
                    current_output += 1
                    current_size = 0
                    
        except IOError:
            print(f"Warning: Could not read file {filepath}")
            continue

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

print(f"Merged files into {NUM_OUTPUT_FILES} files of approximately {target_size/1024/1024:.2f} MB each")

Run Code Interpreter

Running the following program: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

# 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 total size of all files
total_size = 0
file_sizes = []
for path in file_paths:
    try:
        size = os.path.getsize(path)
        total_size += size
        file_sizes.append((path, size))
    except OSError:
        print(f"Warning: Could not access file {path}")
        continue

# Calculate target size per output file
target_size = total_size / NUM_OUTPUT_FILES

# Distribute files across output files
current_output = 0
current_size = 0

output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(NUM_OUTPUT_FILES)]

try:
    for filepath, size in file_sizes:
        try:
            with open(filepath, 'rb') as infile:
                data = infile.read()
                output_files[current_output].write(data)
                current_size += size
                
                # If current output file is approximately at target size, move to next
                if current_size >= target_size and current_output < NUM_OUTPUT_FILES - 1:
                    current_output += 1
                    current_size = 0
                    
        except IOError:
            print(f"Warning: Could not read file {filepath}")
            continue

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

print(f"Merged files into {NUM_OUTPUT_FILES} files of approximately {target_size/1024/1024:.2f} MB each")
And got the output: Merged files into 16 files of approximately 0.03 MB each

PyFunc

final: False Resulting in output: False

Docker Setup

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

Initial Query

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

LLM Generation

Query

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

Output

Here's a Python script that will distribute the contents of files listed in /tmp/files into 16 merged files of approximately equal size: 

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import os

NUM_OUTPUT_FILES = 16

# 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 file sizes and total size
file_sizes = []
total_size = 0
for path in file_paths:
    try:
        size = os.path.getsize(path)
        file_sizes.append((path, size))
        total_size += size
    except OSError:
        print(f"Warning: Could not access file {path}")
        continue

# Calculate target size per output file
target_size = total_size / NUM_OUTPUT_FILES

# Sort files by size (largest first) for better distribution
file_sizes.sort(key=lambda x: x[1], reverse=True)

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

# Distribute files to output buckets
for file_path, file_size in file_sizes:
    # Find the output file with smallest current size
    output_file, current_size = min(output_files, key=lambda x: x[1])
    
    # Add file to the selected output
    output_idx = int(output_file.split('_')[1])
    
    try:
        with open(file_path, 'rb') as input_file:
            with open(output_file, 'ab') as output:
                output.write(input_file.read())
                output.write(b'\n')  # Add newline between files
    except IOError as e:
        print(f"Error processing file {file_path}: {e}")
        continue
    
    # Update size tracking
    output_files[output_idx] = (output_file, current_size + file_size)

# Print final size distribution
for output_file, size in output_files:
    print(f"{output_file}: {size / 1024 / 1024:.2f} MB")
This script: 1. Reads the list of file paths from /tmp/files 2. Calculates the size of each input file and the total size 3. Determines the target size for each output file 4. Sorts files by size (largest first) to help achieve better balance 5. Creates 16 output files named /tmp/merged_0 through /tmp/merged_15 6. Distributes the files by always adding to the output file that currently has the smallest size 7. Copies file contents in binary mode to handle any file type 8. Adds a newline between concatenated files 9. Prints the final size distribution of the output files The script uses a "greedy" approach for distribution, which while not perfect, should give reasonably balanced results. It handles errors gracefully and provides feedback about the final size distribution. Some notes: - The script handles binary files (uses 'rb' and 'ab' modes) - It adds error handling for file access issues - It maintains the original content exactly as-is - It separates concatenated files with newlines - It provides feedback about the final size distribution You can run this script directly, and it will create the 16 merged files in /tmp/. The files will be approximately equal in size, though there might be some variation depending on the original file sizes and distribution.

Extract Code

I extracted the following code from that output: 

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import os

NUM_OUTPUT_FILES = 16

# 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 file sizes and total size
file_sizes = []
total_size = 0
for path in file_paths:
    try:
        size = os.path.getsize(path)
        file_sizes.append((path, size))
        total_size += size
    except OSError:
        print(f"Warning: Could not access file {path}")
        continue

# Calculate target size per output file
target_size = total_size / NUM_OUTPUT_FILES

# Sort files by size (largest first) for better distribution
file_sizes.sort(key=lambda x: x[1], reverse=True)

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

# Distribute files to output buckets
for file_path, file_size in file_sizes:
    # Find the output file with smallest current size
    output_file, current_size = min(output_files, key=lambda x: x[1])
    
    # Add file to the selected output
    output_idx = int(output_file.split('_')[1])
    
    try:
        with open(file_path, 'rb') as input_file:
            with open(output_file, 'ab') as output:
                output.write(input_file.read())
                output.write(b'\n')  # Add newline between files
    except IOError as e:
        print(f"Error processing file {file_path}: {e}")
        continue
    
    # Update size tracking
    output_files[output_idx] = (output_file, current_size + file_size)

# Print final size distribution
for output_file, size in output_files:
    print(f"{output_file}: {size / 1024 / 1024:.2f} MB")

Run Code Interpreter

Running the following program: 

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import os

NUM_OUTPUT_FILES = 16

# 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 file sizes and total size
file_sizes = []
total_size = 0
for path in file_paths:
    try:
        size = os.path.getsize(path)
        file_sizes.append((path, size))
        total_size += size
    except OSError:
        print(f"Warning: Could not access file {path}")
        continue

# Calculate target size per output file
target_size = total_size / NUM_OUTPUT_FILES

# Sort files by size (largest first) for better distribution
file_sizes.sort(key=lambda x: x[1], reverse=True)

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

# Distribute files to output buckets
for file_path, file_size in file_sizes:
    # Find the output file with smallest current size
    output_file, current_size = min(output_files, key=lambda x: x[1])
    
    # Add file to the selected output
    output_idx = int(output_file.split('_')[1])
    
    try:
        with open(file_path, 'rb') as input_file:
            with open(output_file, 'ab') as output:
                output.write(input_file.read())
                output.write(b'\n')  # Add newline between files
    except IOError as e:
        print(f"Error processing file {file_path}: {e}")
        continue
    
    # Update size tracking
    output_files[output_idx] = (output_file, current_size + file_size)

# Print final size distribution
for output_file, size in output_files:
    print(f"{output_file}: {size / 1024 / 1024:.2f} MB")
And got the output: /tmp/merged_0: 0.04 MB /tmp/merged_1: 0.04 MB /tmp/merged_2: 0.04 MB /tmp/merged_3: 0.04 MB /tmp/merged_4: 0.04 MB /tmp/merged_5: 0.04 MB /tmp/merged_6: 0.04 MB /tmp/merged_7: 0.04 MB /tmp/merged_8: 0.04 MB /tmp/merged_9: 0.04 MB /tmp/merged_10: 0.04 MB /tmp/merged_11: 0.04 MB /tmp/merged_12: 0.04 MB /tmp/merged_13: 0.04 MB /tmp/merged_14: 0.04 MB /tmp/merged_15: 0.04 MB

PyFunc

final: True Resulting in output: True

Docker Setup

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

Initial Query

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

LLM Generation

Query

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

Output

Here's a Python script that will merge the contents of files into 16 approximately equal-sized output files: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

def get_file_sizes(file_list_path):
    """Get sizes of all input files."""
    sizes = []
    with open(file_list_path, 'r') as f:
        for filepath in f:
            filepath = filepath.strip()
            try:
                sizes.append((filepath, os.path.getsize(filepath)))
            except OSError:
                print(f"Warning: Could not get size for {filepath}")
    return sizes

def distribute_files(files_with_sizes):
    """Distribute files among output buckets to balance total sizes."""
    # Sort files by size in descending order
    sorted_files = sorted(files_with_sizes, key=lambda x: x[1], reverse=True)
    
    # Initialize buckets
    buckets = [[] for _ in range(NUM_OUTPUT_FILES)]
    bucket_sizes = [0] * NUM_OUTPUT_FILES
    
    # Distribute files using a greedy approach
    for filepath, size in sorted_files:
        # Find bucket with minimum current size
        min_bucket_idx = bucket_sizes.index(min(bucket_sizes))
        buckets[min_bucket_idx].append(filepath)
        bucket_sizes[min_bucket_idx] += size
    
    return buckets

def merge_files(buckets):
    """Merge files according to the calculated distribution."""
    for i, bucket in enumerate(buckets):
        output_path = f"/tmp/merged_{i}"
        print(f"Creating {output_path}")
        
        with open(output_path, 'wb') as outfile:
            for filepath in bucket:
                try:
                    with open(filepath, 'rb') as infile:
                        outfile.write(infile.read())
                except IOError as e:
                    print(f"Error processing {filepath}: {e}")

def main():
    file_list_path = "/tmp/files"
    
    # Get sizes of all input files
    print("Calculating file sizes...")
    files_with_sizes = get_file_sizes(file_list_path)
    
    # Distribute files among buckets
    print("Distributing files...")
    buckets = distribute_files(files_with_sizes)
    
    # Merge files according to distribution
    print("Merging files...")
    merge_files(buckets)
    
    print("Done!")

if __name__ == "__main__":
    main()
This script: 1. Reads the list of file paths from `/tmp/files` 2. Gets the size of each input file 3. Uses a greedy algorithm to distribute the files among 16 buckets, trying to keep the total size of each bucket approximately equal 4. Merges the files in each bucket into the corresponding output file The distribution algorithm: - Sorts files by size in descending order - Places each file into the bucket that currently has the smallest total size Features: - Handles binary files correctly - Includes error handling for missing or unreadable files - Provides progress feedback - Tries to create approximately equal-sized output files To use: 1. Save the script (e.g., as `merge_files.py`) 2. Make sure `/tmp/files` contains your list of file paths 3. Run: `python merge_files.py` The script will create 16 files named `/tmp/merged_0` through `/tmp/merged_15`, each containing the merged contents of their assigned input files. Note: This script loads each file entirely into memory when copying. If you're dealing with very large files, you might want to modify the merge_files function to use buffered reading/writing instead: 
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def merge_files(buckets):
    """Merge files according to the calculated distribution."""
    buffer_size = 1024 * 1024  # 1MB buffer
    for i, bucket in enumerate(buckets):
        output_path = f"/tmp/merged_{i}"
        print(f"Creating {output_path}")
        
        with open(output_path, 'wb') as outfile:
            for filepath in bucket:
                try:
                    with open(filepath, 'rb') as infile:
                        while True:
                            buffer = infile.read(buffer_size)
                            if not buffer:
                                break
                            outfile.write(buffer)
                except IOError as e:
                    print(f"Error processing {filepath}: {e}")
This version uses a buffer to read and write files in chunks, which is more memory-efficient for large files.

Extract Code

I extracted the following code from that output: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

def get_file_sizes(file_list_path):
    """Get sizes of all input files."""
    sizes = []
    with open(file_list_path, 'r') as f:
        for filepath in f:
            filepath = filepath.strip()
            try:
                sizes.append((filepath, os.path.getsize(filepath)))
            except OSError:
                print(f"Warning: Could not get size for {filepath}")
    return sizes

def distribute_files(files_with_sizes):
    """Distribute files among output buckets to balance total sizes."""
    # Sort files by size in descending order
    sorted_files = sorted(files_with_sizes, key=lambda x: x[1], reverse=True)
    
    # Initialize buckets
    buckets = [[] for _ in range(NUM_OUTPUT_FILES)]
    bucket_sizes = [0] * NUM_OUTPUT_FILES
    
    # Distribute files using a greedy approach
    for filepath, size in sorted_files:
        # Find bucket with minimum current size
        min_bucket_idx = bucket_sizes.index(min(bucket_sizes))
        buckets[min_bucket_idx].append(filepath)
        bucket_sizes[min_bucket_idx] += size
    
    return buckets

def merge_files(buckets):
    """Merge files according to the calculated distribution."""
    buffer_size = 1024 * 1024  # 1MB buffer
    for i, bucket in enumerate(buckets):
        output_path = f"/tmp/merged_{i}"
        print(f"Creating {output_path}")
        
        with open(output_path, 'wb') as outfile:
            for filepath in bucket:
                try:
                    with open(filepath, 'rb') as infile:
                        while True:
                            buffer = infile.read(buffer_size)
                            if not buffer:
                                break
                            outfile.write(buffer)
                except IOError as e:
                    print(f"Error processing {filepath}: {e}")

def main():
    file_list_path = "/tmp/files"
    
    # Get sizes of all input files
    print("Calculating file sizes...")
    files_with_sizes = get_file_sizes(file_list_path)
    
    # Distribute files among buckets
    print("Distributing files...")
    buckets = distribute_files(files_with_sizes)
    
    # Merge files according to distribution
    print("Merging files...")
    merge_files(buckets)
    
    print("Done!")

if __name__ == "__main__":
    main()

Run Code Interpreter

Running the following program: 

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import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

def get_file_sizes(file_list_path):
    """Get sizes of all input files."""
    sizes = []
    with open(file_list_path, 'r') as f:
        for filepath in f:
            filepath = filepath.strip()
            try:
                sizes.append((filepath, os.path.getsize(filepath)))
            except OSError:
                print(f"Warning: Could not get size for {filepath}")
    return sizes

def distribute_files(files_with_sizes):
    """Distribute files among output buckets to balance total sizes."""
    # Sort files by size in descending order
    sorted_files = sorted(files_with_sizes, key=lambda x: x[1], reverse=True)
    
    # Initialize buckets
    buckets = [[] for _ in range(NUM_OUTPUT_FILES)]
    bucket_sizes = [0] * NUM_OUTPUT_FILES
    
    # Distribute files using a greedy approach
    for filepath, size in sorted_files:
        # Find bucket with minimum current size
        min_bucket_idx = bucket_sizes.index(min(bucket_sizes))
        buckets[min_bucket_idx].append(filepath)
        bucket_sizes[min_bucket_idx] += size
    
    return buckets

def merge_files(buckets):
    """Merge files according to the calculated distribution."""
    buffer_size = 1024 * 1024  # 1MB buffer
    for i, bucket in enumerate(buckets):
        output_path = f"/tmp/merged_{i}"
        print(f"Creating {output_path}")
        
        with open(output_path, 'wb') as outfile:
            for filepath in bucket:
                try:
                    with open(filepath, 'rb') as infile:
                        while True:
                            buffer = infile.read(buffer_size)
                            if not buffer:
                                break
                            outfile.write(buffer)
                except IOError as e:
                    print(f"Error processing {filepath}: {e}")

def main():
    file_list_path = "/tmp/files"
    
    # Get sizes of all input files
    print("Calculating file sizes...")
    files_with_sizes = get_file_sizes(file_list_path)
    
    # Distribute files among buckets
    print("Distributing files...")
    buckets = distribute_files(files_with_sizes)
    
    # Merge files according to distribution
    print("Merging files...")
    merge_files(buckets)
    
    print("Done!")

if __name__ == "__main__":
    main()
And got the output: Calculating file sizes... Distributing files... Merging files... Creating /tmp/merged_0 Creating /tmp/merged_1 Creating /tmp/merged_2 Creating /tmp/merged_3 Creating /tmp/merged_4 Creating /tmp/merged_5 Creating /tmp/merged_6 Creating /tmp/merged_7 Creating /tmp/merged_8 Creating /tmp/merged_9 Creating /tmp/merged_10 Creating /tmp/merged_11 Creating /tmp/merged_12 Creating /tmp/merged_13 Creating /tmp/merged_14 Creating /tmp/merged_15 Done!

PyFunc

final: True Resulting in output: True

Docker Setup

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

Initial Query

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

LLM Generation

Query

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

Output

Here's a Python script that will merge the contents of files listed in /tmp/files into 16 evenly-sized output files: 

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#!/usr/bin/env python3

import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

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

# Calculate total size of all input files
total_size = 0
file_sizes = []
for filepath in input_files:
    try:
        size = os.path.getsize(filepath)
        total_size += size
        file_sizes.append((filepath, size))
    except OSError:
        print(f"Warning: Could not access file {filepath}")
        continue

# Calculate target size for each output file
target_size_per_file = total_size / NUM_OUTPUT_FILES

# Initialize output files and their current sizes
output_files = {i: {'size': 0, 'path': f'/tmp/merged_{i}'} for i in range(NUM_OUTPUT_FILES)}

# Sort files by size in descending order for better distribution
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Distribute files to output buckets
for filepath, size in file_sizes:
    # Find the output file with the smallest current size
    target_output = min(output_files.items(), key=lambda x: x[1]['size'])[0]
    
    try:
        # Read input file
        with open(filepath, 'rb') as infile:
            content = infile.read()
            
        # Append to output file
        with open(output_files[target_output]['path'], 'ab') as outfile:
            outfile.write(content)
            
        # Update size tracking
        output_files[target_output]['size'] += size
        
    except IOError as e:
        print(f"Error processing file {filepath}: {e}")
        continue

# Print statistics
print("\nMerge complete. Output file sizes:")
for i in range(NUM_OUTPUT_FILES):
    size_mb = output_files[i]['size'] / (1024 * 1024)
    print(f"/tmp/merged_{i}: {size_mb:.2f} MB")
This script: 1. Reads the list of input files from /tmp/files 2. Calculates the total size of all input files 3. Sorts the files by size in descending order (this helps achieve better balance) 4. Distributes the files across 16 output files, always adding to the output file that currently has the least content 5. Uses binary mode for reading and writing to handle any type of file content 6. Includes error handling for file access issues 7. Prints statistics about the final size of each output file The script tries to achieve approximately equal sizes by always adding to the smallest output file. While the sizes won't be exactly equal (due to the discrete nature of files), they should be reasonably balanced. To use the script: 1. Save it to a file (e.g., `merge_files.py`) 2. Make it executable: `chmod +x merge_files.py` 3. Run it: `./merge_files.py` The script will create 16 files named `/tmp/merged_0` through `/tmp/merged_15`, each containing the merged contents of a subset of the input files. Note: Make sure you have enough disk space in /tmp to store the merged files, as they will collectively be the same size as all input files combined.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3

import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

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

# Calculate total size of all input files
total_size = 0
file_sizes = []
for filepath in input_files:
    try:
        size = os.path.getsize(filepath)
        total_size += size
        file_sizes.append((filepath, size))
    except OSError:
        print(f"Warning: Could not access file {filepath}")
        continue

# Calculate target size for each output file
target_size_per_file = total_size / NUM_OUTPUT_FILES

# Initialize output files and their current sizes
output_files = {i: {'size': 0, 'path': f'/tmp/merged_{i}'} for i in range(NUM_OUTPUT_FILES)}

# Sort files by size in descending order for better distribution
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Distribute files to output buckets
for filepath, size in file_sizes:
    # Find the output file with the smallest current size
    target_output = min(output_files.items(), key=lambda x: x[1]['size'])[0]
    
    try:
        # Read input file
        with open(filepath, 'rb') as infile:
            content = infile.read()
            
        # Append to output file
        with open(output_files[target_output]['path'], 'ab') as outfile:
            outfile.write(content)
            
        # Update size tracking
        output_files[target_output]['size'] += size
        
    except IOError as e:
        print(f"Error processing file {filepath}: {e}")
        continue

# Print statistics
print("\nMerge complete. Output file sizes:")
for i in range(NUM_OUTPUT_FILES):
    size_mb = output_files[i]['size'] / (1024 * 1024)
    print(f"/tmp/merged_{i}: {size_mb:.2f} MB")

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3

import os
from pathlib import Path

NUM_OUTPUT_FILES = 16

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

# Calculate total size of all input files
total_size = 0
file_sizes = []
for filepath in input_files:
    try:
        size = os.path.getsize(filepath)
        total_size += size
        file_sizes.append((filepath, size))
    except OSError:
        print(f"Warning: Could not access file {filepath}")
        continue

# Calculate target size for each output file
target_size_per_file = total_size / NUM_OUTPUT_FILES

# Initialize output files and their current sizes
output_files = {i: {'size': 0, 'path': f'/tmp/merged_{i}'} for i in range(NUM_OUTPUT_FILES)}

# Sort files by size in descending order for better distribution
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Distribute files to output buckets
for filepath, size in file_sizes:
    # Find the output file with the smallest current size
    target_output = min(output_files.items(), key=lambda x: x[1]['size'])[0]
    
    try:
        # Read input file
        with open(filepath, 'rb') as infile:
            content = infile.read()
            
        # Append to output file
        with open(output_files[target_output]['path'], 'ab') as outfile:
            outfile.write(content)
            
        # Update size tracking
        output_files[target_output]['size'] += size
        
    except IOError as e:
        print(f"Error processing file {filepath}: {e}")
        continue

# Print statistics
print("\nMerge complete. Output file sizes:")
for i in range(NUM_OUTPUT_FILES):
    size_mb = output_files[i]['size'] / (1024 * 1024)
    print(f"/tmp/merged_{i}: {size_mb:.2f} MB")
And got the output: Merge complete. Output file sizes: /tmp/merged_0: 0.04 MB /tmp/merged_1: 0.04 MB /tmp/merged_2: 0.04 MB /tmp/merged_3: 0.04 MB /tmp/merged_4: 0.04 MB /tmp/merged_5: 0.04 MB /tmp/merged_6: 0.04 MB /tmp/merged_7: 0.04 MB /tmp/merged_8: 0.04 MB /tmp/merged_9: 0.04 MB /tmp/merged_10: 0.04 MB /tmp/merged_11: 0.04 MB /tmp/merged_12: 0.04 MB /tmp/merged_13: 0.04 MB /tmp/merged_14: 0.04 MB /tmp/merged_15: 0.04 MB

PyFunc

final: True Resulting in output: True

Docker Setup

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

Initial Query

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

LLM Generation

Query

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

Output

Here's a Python script that will distribute the contents of files into 16 merged files of approximately equal size: 

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import os

NUM_OUTPUT_FILES = 16

def get_file_sizes(file_list_path):
    """Get sizes of all files from the list."""
    sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            filepath = line.strip()
            try:
                sizes.append((filepath, os.path.getsize(filepath)))
            except (OSError, IOError):
                print(f"Warning: Could not get size for {filepath}")
    return sizes

def distribute_files(files_with_sizes):
    """Distribute files into NUM_OUTPUT_FILES buckets trying to balance total size."""
    # Sort files by size in descending order
    sorted_files = sorted(files_with_sizes, key=lambda x: x[1], reverse=True)
    
    # Initialize buckets
    buckets = [[] for _ in range(NUM_OUTPUT_FILES)]
    bucket_sizes = [0] * NUM_OUTPUT_FILES
    
    # Distribute files using greedy approach
    for filepath, size in sorted_files:
        # Find bucket with minimum current size
        min_bucket_idx = bucket_sizes.index(min(bucket_sizes))
        buckets[min_bucket_idx].append(filepath)
        bucket_sizes[min_bucket_idx] += size
    
    return buckets

def merge_files(buckets):
    """Merge files according to the calculated distribution."""
    for i, bucket in enumerate(buckets):
        output_path = f"/tmp/merged_{i}"
        with open(output_path, 'wb') as outfile:
            for filepath in bucket:
                try:
                    with open(filepath, 'rb') as infile:
                        outfile.write(infile.read())
                except (OSError, IOError) as e:
                    print(f"Warning: Error processing {filepath}: {e}")

def main():
    file_list_path = "/tmp/files"
    
    # Get file sizes
    print("Getting file sizes...")
    files_with_sizes = get_file_sizes(file_list_path)
    
    # Distribute files
    print("Calculating distribution...")
    buckets = distribute_files(files_with_sizes)
    
    # Merge files
    print("Merging files...")
    merge_files(buckets)
    
    # Print statistics
    print("\nDistribution statistics:")
    for i, bucket in enumerate(buckets):
        total_size = sum(os.path.getsize(f) for f in bucket)
        print(f"merged_{i}: {len(bucket)} files, {total_size/1024/1024:.2f} MB")

if __name__ == "__main__":
    main()
This script: 1. Reads the list of file paths from `/tmp/files` 2. Gets the size of each file 3. Uses a greedy algorithm to distribute the files into 16 buckets trying to keep the total size of each bucket approximately equal 4. Merges the files in each bucket into the corresponding output file 5. Prints statistics about the distribution The script handles files in binary mode to support both text and binary files. It also includes basic error handling for cases where files might be inaccessible or have changed since the list was created. Features: - Handles large files efficiently by reading/writing in binary mode - Tries to create output files of similar sizes - Provides progress feedback and final statistics - Includes error handling for common file operations - Preserves file contents exactly as they are To use it, simply run the script after ensuring that `/tmp/files` contains the list of file paths. The script will create 16 files named `/tmp/merged_0` through `/tmp/merged_15`. Note: Make sure you have read permissions for all input files and write permission for the `/tmp` directory.

Extract Code

I extracted the following code from that output: 

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import os

NUM_OUTPUT_FILES = 16

def get_file_sizes(file_list_path):
    """Get sizes of all files from the list."""
    sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            filepath = line.strip()
            try:
                sizes.append((filepath, os.path.getsize(filepath)))
            except (OSError, IOError):
                print(f"Warning: Could not get size for {filepath}")
    return sizes

def distribute_files(files_with_sizes):
    """Distribute files into NUM_OUTPUT_FILES buckets trying to balance total size."""
    # Sort files by size in descending order
    sorted_files = sorted(files_with_sizes, key=lambda x: x[1], reverse=True)
    
    # Initialize buckets
    buckets = [[] for _ in range(NUM_OUTPUT_FILES)]
    bucket_sizes = [0] * NUM_OUTPUT_FILES
    
    # Distribute files using greedy approach
    for filepath, size in sorted_files:
        # Find bucket with minimum current size
        min_bucket_idx = bucket_sizes.index(min(bucket_sizes))
        buckets[min_bucket_idx].append(filepath)
        bucket_sizes[min_bucket_idx] += size
    
    return buckets

def merge_files(buckets):
    """Merge files according to the calculated distribution."""
    for i, bucket in enumerate(buckets):
        output_path = f"/tmp/merged_{i}"
        with open(output_path, 'wb') as outfile:
            for filepath in bucket:
                try:
                    with open(filepath, 'rb') as infile:
                        outfile.write(infile.read())
                except (OSError, IOError) as e:
                    print(f"Warning: Error processing {filepath}: {e}")

def main():
    file_list_path = "/tmp/files"
    
    # Get file sizes
    print("Getting file sizes...")
    files_with_sizes = get_file_sizes(file_list_path)
    
    # Distribute files
    print("Calculating distribution...")
    buckets = distribute_files(files_with_sizes)
    
    # Merge files
    print("Merging files...")
    merge_files(buckets)
    
    # Print statistics
    print("\nDistribution statistics:")
    for i, bucket in enumerate(buckets):
        total_size = sum(os.path.getsize(f) for f in bucket)
        print(f"merged_{i}: {len(bucket)} files, {total_size/1024/1024:.2f} MB")

if __name__ == "__main__":
    main()

Run Code Interpreter

Running the following program: 

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import os

NUM_OUTPUT_FILES = 16

def get_file_sizes(file_list_path):
    """Get sizes of all files from the list."""
    sizes = []
    with open(file_list_path, 'r') as f:
        for line in f:
            filepath = line.strip()
            try:
                sizes.append((filepath, os.path.getsize(filepath)))
            except (OSError, IOError):
                print(f"Warning: Could not get size for {filepath}")
    return sizes

def distribute_files(files_with_sizes):
    """Distribute files into NUM_OUTPUT_FILES buckets trying to balance total size."""
    # Sort files by size in descending order
    sorted_files = sorted(files_with_sizes, key=lambda x: x[1], reverse=True)
    
    # Initialize buckets
    buckets = [[] for _ in range(NUM_OUTPUT_FILES)]
    bucket_sizes = [0] * NUM_OUTPUT_FILES
    
    # Distribute files using greedy approach
    for filepath, size in sorted_files:
        # Find bucket with minimum current size
        min_bucket_idx = bucket_sizes.index(min(bucket_sizes))
        buckets[min_bucket_idx].append(filepath)
        bucket_sizes[min_bucket_idx] += size
    
    return buckets

def merge_files(buckets):
    """Merge files according to the calculated distribution."""
    for i, bucket in enumerate(buckets):
        output_path = f"/tmp/merged_{i}"
        with open(output_path, 'wb') as outfile:
            for filepath in bucket:
                try:
                    with open(filepath, 'rb') as infile:
                        outfile.write(infile.read())
                except (OSError, IOError) as e:
                    print(f"Warning: Error processing {filepath}: {e}")

def main():
    file_list_path = "/tmp/files"
    
    # Get file sizes
    print("Getting file sizes...")
    files_with_sizes = get_file_sizes(file_list_path)
    
    # Distribute files
    print("Calculating distribution...")
    buckets = distribute_files(files_with_sizes)
    
    # Merge files
    print("Merging files...")
    merge_files(buckets)
    
    # Print statistics
    print("\nDistribution statistics:")
    for i, bucket in enumerate(buckets):
        total_size = sum(os.path.getsize(f) for f in bucket)
        print(f"merged_{i}: {len(bucket)} files, {total_size/1024/1024:.2f} MB")

if __name__ == "__main__":
    main()
And got the output: Getting file sizes... Calculating distribution... Merging files... Distribution statistics: merged_0: 45 files, 0.04 MB merged_1: 48 files, 0.04 MB merged_2: 48 files, 0.04 MB merged_3: 53 files, 0.04 MB merged_4: 53 files, 0.04 MB merged_5: 68 files, 0.04 MB merged_6: 68 files, 0.04 MB merged_7: 69 files, 0.04 MB merged_8: 68 files, 0.04 MB merged_9: 68 files, 0.04 MB merged_10: 68 files, 0.04 MB merged_11: 69 files, 0.04 MB merged_12: 69 files, 0.04 MB merged_13: 68 files, 0.04 MB merged_14: 69 files, 0.04 MB merged_15: 69 files, 0.04 MB

PyFunc

final: True Resulting in output: True