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 is a Python script that accomplishes the task you described: 

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import os
import math

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

# calculate total size of all files
total_size = sum(os.path.getsize(f) for f in file_paths)

# calculate target size for each merged file
target_size = math.ceil(total_size / 16)

# initialize merged files
merged_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
merged_sizes = [0] * 16

# sort files by size, largest first
file_paths.sort(key=os.path.getsize, reverse=True)

# add files to merged files in order, always choosing the smallest one
for file_path in file_paths:
    file_size = os.path.getsize(file_path)
    min_index = min(range(16), key=lambda i: merged_sizes[i])
    merged_files[min_index].write(open(file_path, 'rb').read())
    merged_sizes[min_index] += file_size

# close merged files
for merged_file in merged_files:
    merged_file.close()
This script reads the list of file paths from `/tmp/files`, calculates the total size of all files, and then calculates the target size for each of the 16 merged files. It then initializes the merged files and sorts the original files by size, largest first. It then iterates through the original files, adding each one to the merged file that currently has the smallest size, until all files have been added. Note that this script does not handle the case where the total size of the files is not evenly divisible by 16, so some of the merged files may end up being slightly larger or smaller than the target size. Also, this script assumes that all the files are present in the system and readable. Also, please be aware that this script will use a lot of memory if the files are big. If the files are too big to fit in memory, you may need to use a different approach, such as reading and writing the files in chunks instead of all at once.

Extract Code

I extracted the following code from that output: 

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import os
import math

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

# calculate total size of all files
total_size = sum(os.path.getsize(f) for f in file_paths)

# calculate target size for each merged file
target_size = math.ceil(total_size / 16)

# initialize merged files
merged_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
merged_sizes = [0] * 16

# sort files by size, largest first
file_paths.sort(key=os.path.getsize, reverse=True)

# add files to merged files in order, always choosing the smallest one
for file_path in file_paths:
    file_size = os.path.getsize(file_path)
    min_index = min(range(16), key=lambda i: merged_sizes[i])
    merged_files[min_index].write(open(file_path, 'rb').read())
    merged_sizes[min_index] += file_size

# close merged files
for merged_file in merged_files:
    merged_file.close()

Run Code Interpreter

Running the following program: 

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import os
import math

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

# calculate total size of all files
total_size = sum(os.path.getsize(f) for f in file_paths)

# calculate target size for each merged file
target_size = math.ceil(total_size / 16)

# initialize merged files
merged_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
merged_sizes = [0] * 16

# sort files by size, largest first
file_paths.sort(key=os.path.getsize, reverse=True)

# add files to merged files in order, always choosing the smallest one
for file_path in file_paths:
    file_size = os.path.getsize(file_path)
    min_index = min(range(16), key=lambda i: merged_sizes[i])
    merged_files[min_index].write(open(file_path, 'rb').read())
    merged_sizes[min_index] += file_size

# close merged files
for merged_file in merged_files:
    merged_file.close()
And got the output:

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 does what you asked for: 

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import os
import math

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

# Get the size of each file and sort the list by size in descending order
file_sizes = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize the merged files with zero size
merged_files = [{'path': '/tmp/merged_{}'.format(i), 'size': 0} for i in range(16)]

# Distribute the files among the merged files
for file_path, file_size in file_sizes:
    # Find the merged file with the smallest size
    smallest_index = min(range(16), key=lambda i: merged_files[i]['size'])
    
    # Add the file size to the merged file
    merged_files[smallest_index]['size'] += file_size
    
    # Write the file to the merged file
    with open(merged_files[smallest_index]['path'], 'ab') as f:
        with open(file_path, 'rb') as g:
            f.write(g.read())

# Pad the merged files to make them approximately the same size
total_size = sum(f['size'] for f in merged_files)
avg_size = math.ceil(total_size / 16)
for i, file_info in enumerate(merged_files):
    with open(file_info['path'], 'ab') as f:
        f.seek(file_info['size'])
        padding = b' ' * (avg_size - file_info['size'])
        f.write(padding)
The script first reads the list of file paths from `/tmp/files`, gets the size of each file, and sorts the list by size in descending order. It initializes the merged files with zero size and then distributes the files among the merged files by always adding the next file to the merged file with the smallest size. Finally, it pads the merged files with spaces to make them approximately the same size. Note that the padding may cause some merged files to be slightly larger than others. Also, note that this script assumes that the files in `/tmp/files` are all in the same directory. If the files are in different directories, you may need to modify the script to handle the different paths.

Extract Code

I extracted the following code from that output: 

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import os
import math

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

# Get the size of each file and sort the list by size in descending order
file_sizes = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize the merged files with zero size
merged_files = [{'path': '/tmp/merged_{}'.format(i), 'size': 0} for i in range(16)]

# Distribute the files among the merged files
for file_path, file_size in file_sizes:
    # Find the merged file with the smallest size
    smallest_index = min(range(16), key=lambda i: merged_files[i]['size'])
    
    # Add the file size to the merged file
    merged_files[smallest_index]['size'] += file_size
    
    # Write the file to the merged file
    with open(merged_files[smallest_index]['path'], 'ab') as f:
        with open(file_path, 'rb') as g:
            f.write(g.read())

# Pad the merged files to make them approximately the same size
total_size = sum(f['size'] for f in merged_files)
avg_size = math.ceil(total_size / 16)
for i, file_info in enumerate(merged_files):
    with open(file_info['path'], 'ab') as f:
        f.seek(file_info['size'])
        padding = b' ' * (avg_size - file_info['size'])
        f.write(padding)

Run Code Interpreter

Running the following program: 

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import os
import math

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

# Get the size of each file and sort the list by size in descending order
file_sizes = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize the merged files with zero size
merged_files = [{'path': '/tmp/merged_{}'.format(i), 'size': 0} for i in range(16)]

# Distribute the files among the merged files
for file_path, file_size in file_sizes:
    # Find the merged file with the smallest size
    smallest_index = min(range(16), key=lambda i: merged_files[i]['size'])
    
    # Add the file size to the merged file
    merged_files[smallest_index]['size'] += file_size
    
    # Write the file to the merged file
    with open(merged_files[smallest_index]['path'], 'ab') as f:
        with open(file_path, 'rb') as g:
            f.write(g.read())

# Pad the merged files to make them approximately the same size
total_size = sum(f['size'] for f in merged_files)
avg_size = math.ceil(total_size / 16)
for i, file_info in enumerate(merged_files):
    with open(file_info['path'], 'ab') as f:
        f.seek(file_info['size'])
        padding = b' ' * (avg_size - file_info['size'])
        f.write(padding)
And got the output:

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: 

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import os
import sys
import math
from collections import defaultdict

# Function to read file and return its content and size
def read_file(file_path):
    with open(file_path, 'rb') as f:
        content = f.read()
        size = len(content)
    return content, size

# Function to write data to a file
def write_to_file(file_path, data):
    with open(file_path, 'ab') as f:
        f.write(data)

# Read the list of file paths
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 = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize merged files with their file handles
merged_files = defaultdict(lambda: open(f'/tmp/merged_{hash(os.times()) % 16}', 'ab'))

# Add files to the merged files
total_size = 0
for path, size in file_sizes:
    content, size = read_file(path)
    target_file, target_size = min([(f, s) for f, s in merged_files.items()], key=lambda x: x[1])
    merged_files[target_file].write(content)
    merged_files[target_file] = open(f'/tmp/merged_{hash(os.times()) % 16}', 'ab') if target_size + size > 1.2 * total_size / 16 else target_file
    total_size += size

# Close all merged files
for file_handle in merged_files.values():
    file_handle.close()
This script reads the file paths from `/tmp/files`, sorts them by size, and then distributes their content among 16 merged files. The merged files are named `/tmp/merged_X`, where `X` is a number between 0 and 15. To ensure that each merged file is approximately the same size, the script tries to add the content of the next file to the merged file with the smallest current size. However, to prevent a single merged file from becoming too large, the script occasionally switches to a new merged file when the current one is more than 1.2 times the average size of all merged files so far. Note that this script uses a simple hash function to generate unique file names for the merged files. If you need to ensure that the merged files are named in a specific order, you can replace this hash function with a counter or another naming scheme. Also note that this script does not handle errors or edge cases, such as files that are too large to fit in memory or files that are locked by other processes. In a production environment, you may want to add error handling and logging to make the script more robust.

Extract Code

I extracted the following code from that output: 

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import os
import sys
import math
from collections import defaultdict

# Function to read file and return its content and size
def read_file(file_path):
    with open(file_path, 'rb') as f:
        content = f.read()
        size = len(content)
    return content, size

# Function to write data to a file
def write_to_file(file_path, data):
    with open(file_path, 'ab') as f:
        f.write(data)

# Read the list of file paths
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 = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize merged files with their file handles
merged_files = defaultdict(lambda: open(f'/tmp/merged_{hash(os.times()) % 16}', 'ab'))

# Add files to the merged files
total_size = 0
for path, size in file_sizes:
    content, size = read_file(path)
    target_file, target_size = min([(f, s) for f, s in merged_files.items()], key=lambda x: x[1])
    merged_files[target_file].write(content)
    merged_files[target_file] = open(f'/tmp/merged_{hash(os.times()) % 16}', 'ab') if target_size + size > 1.2 * total_size / 16 else target_file
    total_size += size

# Close all merged files
for file_handle in merged_files.values():
    file_handle.close()

Run Code Interpreter

Running the following program: 

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import os
import sys
import math
from collections import defaultdict

# Function to read file and return its content and size
def read_file(file_path):
    with open(file_path, 'rb') as f:
        content = f.read()
        size = len(content)
    return content, size

# Function to write data to a file
def write_to_file(file_path, data):
    with open(file_path, 'ab') as f:
        f.write(data)

# Read the list of file paths
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 = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize merged files with their file handles
merged_files = defaultdict(lambda: open(f'/tmp/merged_{hash(os.times()) % 16}', 'ab'))

# Add files to the merged files
total_size = 0
for path, size in file_sizes:
    content, size = read_file(path)
    target_file, target_size = min([(f, s) for f, s in merged_files.items()], key=lambda x: x[1])
    merged_files[target_file].write(content)
    merged_files[target_file] = open(f'/tmp/merged_{hash(os.times()) % 16}', 'ab') if target_size + size > 1.2 * total_size / 16 else target_file
    total_size += size

# Close all merged files
for file_handle in merged_files.values():
    file_handle.close()
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 35, in <module> target_file, target_size = min([(f, s) for f, s in merged_files.items()], key=lambda x: x[1]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ValueError: min() arg is an empty sequence

PyFunc

Traceback (most recent call last): File "/usr/src/app/check.py", line 20, in <module> print('final: ' + str(test())) ^^^^^^ File "/usr/src/app/check.py", line 6, in test sz.append(len(open("/tmp/merged_%d" % i).read())) ^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/tmp/merged_0' 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 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 is a possible solution for your problem: 

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import os
import math

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

# Calculate total size of all files
total_size = sum(os.path.getsize(f) for f in file_paths)

# Get the number of output files
num_files = 16

# Calculate the target size for each output file
target_size = math.ceil(total_size / num_files)

# Initialize a list to keep track of the current size of each output file
file_sizes = [0] * num_files

# Initialize a list to keep track of the output file handles
output_files = [None] * num_files

# Open all output files
for i in range(num_files):
    output_files[i] = open(f'/tmp/merged_{i:02d}', 'wb')

# Sort files by size (largest first)
file_paths.sort(key=os.path.getsize, reverse=True)

# Iterate over all input files and distribute their contents to the output files
for file_path in file_paths:
    # Find the output file with the smallest size
    idx = min(range(num_files), key=lambda i: file_sizes[i])
    
    # Write the contents of the input file to the output file
    with open(file_path, 'rb') as f:
        while True:
            # Read a chunk of data from the input file
            data = f.read(1024 * 1024)
            if not data:
                break
            
            # Calculate the new size of the output file after writing the data
            new_size = file_sizes[idx] + len(data)
            
            # Check if the new size exceeds the target size
            if new_size > target_size:
                # If it does, distribute the remaining data to other output files
                for j in range(num_files):
                    if j == idx or file_sizes[j] >= target_size:
                        continue
                    
                    remaining_size = target_size - file_sizes[j]
                    if remaining_size >= len(data):
                        # If there is enough space in the current output file, write all remaining data
                        output_files[j].write(data)
                        file_sizes[j] += len(data)
                        break
                    else:
                        # Otherwise, write as much data as possible and continue to the next output file
                        output_files[j].write(data[:remaining_size])
                        file_sizes[j] += remaining_size
                        data = data[remaining_size:]
                
                # If we have written all data to other output files, break the loop
                if not data:
                    break
                
                # Otherwise, update the size of the current output file and continue to the next input file
                file_sizes[idx] = target_size
                break
            
            # If the new size does not exceed the target size, write the data to the output file
            output_files[idx].write(data)
            file_sizes[idx] = new_size

# Close all output files
for f in output_files:
    f.close()
This solution first calculates the total size of all input files and then divides it by the number of output files to get the target size for each output file. It then initializes a list to keep track of the current size of each output file and opens all output files. Next, it sorts the input files by size (largest first) and iterates over them. For each input file, it finds the output file with the smallest size and writes its contents to it. If the new size of the output file exceeds the target size, it distributes the remaining data to other output files until all data has been written or all output files have reached their target size. Finally, it closes all output files. Note that this solution assumes that the input files are all located in the same directory and that the output files are all located in the `/tmp` directory. If this is not the case, you may need to modify the paths accordingly. Also note that this solution uses a fixed chunk size of 1 MB to read data from the input files. You may want to adjust this value depending on the size of your input files and the performance of your system.

Extract Code

I extracted the following code from that output: 

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import os
import math

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

# Calculate total size of all files
total_size = sum(os.path.getsize(f) for f in file_paths)

# Get the number of output files
num_files = 16

# Calculate the target size for each output file
target_size = math.ceil(total_size / num_files)

# Initialize a list to keep track of the current size of each output file
file_sizes = [0] * num_files

# Initialize a list to keep track of the output file handles
output_files = [None] * num_files

# Open all output files
for i in range(num_files):
    output_files[i] = open(f'/tmp/merged_{i:02d}', 'wb')

# Sort files by size (largest first)
file_paths.sort(key=os.path.getsize, reverse=True)

# Iterate over all input files and distribute their contents to the output files
for file_path in file_paths:
    # Find the output file with the smallest size
    idx = min(range(num_files), key=lambda i: file_sizes[i])
    
    # Write the contents of the input file to the output file
    with open(file_path, 'rb') as f:
        while True:
            # Read a chunk of data from the input file
            data = f.read(1024 * 1024)
            if not data:
                break
            
            # Calculate the new size of the output file after writing the data
            new_size = file_sizes[idx] + len(data)
            
            # Check if the new size exceeds the target size
            if new_size > target_size:
                # If it does, distribute the remaining data to other output files
                for j in range(num_files):
                    if j == idx or file_sizes[j] >= target_size:
                        continue
                    
                    remaining_size = target_size - file_sizes[j]
                    if remaining_size >= len(data):
                        # If there is enough space in the current output file, write all remaining data
                        output_files[j].write(data)
                        file_sizes[j] += len(data)
                        break
                    else:
                        # Otherwise, write as much data as possible and continue to the next output file
                        output_files[j].write(data[:remaining_size])
                        file_sizes[j] += remaining_size
                        data = data[remaining_size:]
                
                # If we have written all data to other output files, break the loop
                if not data:
                    break
                
                # Otherwise, update the size of the current output file and continue to the next input file
                file_sizes[idx] = target_size
                break
            
            # If the new size does not exceed the target size, write the data to the output file
            output_files[idx].write(data)
            file_sizes[idx] = new_size

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

Run Code Interpreter

Running the following program: 

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import os
import math

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

# Calculate total size of all files
total_size = sum(os.path.getsize(f) for f in file_paths)

# Get the number of output files
num_files = 16

# Calculate the target size for each output file
target_size = math.ceil(total_size / num_files)

# Initialize a list to keep track of the current size of each output file
file_sizes = [0] * num_files

# Initialize a list to keep track of the output file handles
output_files = [None] * num_files

# Open all output files
for i in range(num_files):
    output_files[i] = open(f'/tmp/merged_{i:02d}', 'wb')

# Sort files by size (largest first)
file_paths.sort(key=os.path.getsize, reverse=True)

# Iterate over all input files and distribute their contents to the output files
for file_path in file_paths:
    # Find the output file with the smallest size
    idx = min(range(num_files), key=lambda i: file_sizes[i])
    
    # Write the contents of the input file to the output file
    with open(file_path, 'rb') as f:
        while True:
            # Read a chunk of data from the input file
            data = f.read(1024 * 1024)
            if not data:
                break
            
            # Calculate the new size of the output file after writing the data
            new_size = file_sizes[idx] + len(data)
            
            # Check if the new size exceeds the target size
            if new_size > target_size:
                # If it does, distribute the remaining data to other output files
                for j in range(num_files):
                    if j == idx or file_sizes[j] >= target_size:
                        continue
                    
                    remaining_size = target_size - file_sizes[j]
                    if remaining_size >= len(data):
                        # If there is enough space in the current output file, write all remaining data
                        output_files[j].write(data)
                        file_sizes[j] += len(data)
                        break
                    else:
                        # Otherwise, write as much data as possible and continue to the next output file
                        output_files[j].write(data[:remaining_size])
                        file_sizes[j] += remaining_size
                        data = data[remaining_size:]
                
                # If we have written all data to other output files, break the loop
                if not data:
                    break
                
                # Otherwise, update the size of the current output file and continue to the next input file
                file_sizes[idx] = target_size
                break
            
            # If the new size does not exceed the target size, write the data to the output file
            output_files[idx].write(data)
            file_sizes[idx] = new_size

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

PyFunc

Traceback (most recent call last): File "/usr/src/app/check.py", line 20, in <module> print('final: ' + str(test())) ^^^^^^ File "/usr/src/app/check.py", line 6, in test sz.append(len(open("/tmp/merged_%d" % i).read())) ^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/tmp/merged_0' 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 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 does what you described: 

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import os
import math

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

# Calculate total size and number of chunks
total_size = sum(os.path.getsize(f) for f in file_paths)
num_chunks = 16
chunk_size = math.ceil(total_size / num_chunks)

# Sort files by size (largest first)
file_paths.sort(key=os.path.getsize, reverse=True)

# Initialize empty list of chunks
chunks = [open(f'/tmp/merged_{i:02d}', 'wb') for i in range(num_chunks)]
current_chunk_sizes = [0] * num_chunks

# Add files to chunks in order
for file_path in file_paths:
    file_size = os.path.getsize(file_path)
    # Find the smallest chunk that can fit the current file
    for i, (chunk_size, chunk) in enumerate(zip(current_chunk_sizes, chunks)):
        if chunk_size + file_size <= chunk_size * 2:
            # Write file to chunk
            with open(file_path, 'rb') as f:
                chunk.write(f.read())
            # Update chunk size
            current_chunk_sizes[i] += file_size
            break

# Close all chunks
for chunk in chunks:
    chunk.close()
This script first reads the list of file paths from `/tmp/files` and calculates the total size and number of chunks. It then sorts the files by size (largest first) and initializes an empty list of chunks. Next, the script iterates through the list of files and adds each file to the smallest chunk that can fit it, while ensuring that the size of each chunk does not exceed twice its current size (to avoid creating very large chunks). Finally, the script closes all the chunks. Note that this script assumes that the resulting merged files will fit in the available disk space. If this is not the case, you may need to modify the script to handle this situation appropriately.

Extract Code

I extracted the following code from that output: 

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import os
import math

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

# Calculate total size and number of chunks
total_size = sum(os.path.getsize(f) for f in file_paths)
num_chunks = 16
chunk_size = math.ceil(total_size / num_chunks)

# Sort files by size (largest first)
file_paths.sort(key=os.path.getsize, reverse=True)

# Initialize empty list of chunks
chunks = [open(f'/tmp/merged_{i:02d}', 'wb') for i in range(num_chunks)]
current_chunk_sizes = [0] * num_chunks

# Add files to chunks in order
for file_path in file_paths:
    file_size = os.path.getsize(file_path)
    # Find the smallest chunk that can fit the current file
    for i, (chunk_size, chunk) in enumerate(zip(current_chunk_sizes, chunks)):
        if chunk_size + file_size <= chunk_size * 2:
            # Write file to chunk
            with open(file_path, 'rb') as f:
                chunk.write(f.read())
            # Update chunk size
            current_chunk_sizes[i] += file_size
            break

# Close all chunks
for chunk in chunks:
    chunk.close()

Run Code Interpreter

Running the following program: 

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import os
import math

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

# Calculate total size and number of chunks
total_size = sum(os.path.getsize(f) for f in file_paths)
num_chunks = 16
chunk_size = math.ceil(total_size / num_chunks)

# Sort files by size (largest first)
file_paths.sort(key=os.path.getsize, reverse=True)

# Initialize empty list of chunks
chunks = [open(f'/tmp/merged_{i:02d}', 'wb') for i in range(num_chunks)]
current_chunk_sizes = [0] * num_chunks

# Add files to chunks in order
for file_path in file_paths:
    file_size = os.path.getsize(file_path)
    # Find the smallest chunk that can fit the current file
    for i, (chunk_size, chunk) in enumerate(zip(current_chunk_sizes, chunks)):
        if chunk_size + file_size <= chunk_size * 2:
            # Write file to chunk
            with open(file_path, 'rb') as f:
                chunk.write(f.read())
            # Update chunk size
            current_chunk_sizes[i] += file_size
            break

# Close all chunks
for chunk in chunks:
    chunk.close()
And got the output:

PyFunc

Traceback (most recent call last): File "/usr/src/app/check.py", line 20, in <module> print('final: ' + str(test())) ^^^^^^ File "/usr/src/app/check.py", line 6, in test sz.append(len(open("/tmp/merged_%d" % i).read())) ^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/tmp/merged_0' Resulting in output: False