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 merges the files listed in "/tmp/files", sorts them by size (largest first), and splits the content into 16 files of approximately equal size: 

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import os

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Get the file sizes and sort the file paths by size (largest first)
file_sizes = [(file_path, os.path.getsize(file_path)) for file_path in file_paths]
sorted_files = sorted(file_sizes, key=lambda x: x[1], reverse=True)

# Initialize the merged files
merged_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]

# Initialize the current sizes of the merged files
merged_sizes = [0] * 16

# Merge the files and distribute the content evenly
for file_path, file_size in sorted_files:
    # Find the index of the smallest merged file
    smallest_index = merged_sizes.index(min(merged_sizes))

    # Read the content of the file and write it to the smallest merged file
    with open(file_path, "rb") as file:
        content = file.read()
        merged_files[smallest_index].write(content)

    # Update the size of the smallest merged file
    merged_sizes[smallest_index] += file_size

# Close the merged files
for merged_file in merged_files:
    merged_file.close()
Here's how the script works: 1. It reads the file paths from "/tmp/files" and stores them in the `file_paths` list. 2. It calculates the size of each file using `os.path.getsize()` and creates a list of tuples `file_sizes`, where each tuple contains the file path and its corresponding size. 3. It sorts the `file_sizes` list in descending order based on the file sizes using `sorted()` with a key function that extracts the size from each tuple. The sorted list is stored in `sorted_files`. 4. It initializes 16 merged files using a list comprehension, creating file objects with names like "/tmp/merged_0", "/tmp/merged_1", etc. 5. It initializes a list `merged_sizes` to keep track of the current sizes of the merged files, initially set to zero for all files. 6. It iterates over the sorted file paths and sizes in `sorted_files`. 7. For each file, it finds the index of the smallest merged file by using `merged_sizes.index(min(merged_sizes))`. 8. It reads the content of the file using `open()` in binary mode and writes it to the smallest merged file using `merged_files[smallest_index].write()`. 9. It updates the size of the smallest merged file in `merged_sizes` by adding the size of the current file. 10. Finally, it closes all the merged files using a loop. After running this script, you will have 16 files named "/tmp/merged_0" to "/tmp/merged_15", each containing approximately an equal portion of the merged content from the files listed in "/tmp/files".

Extract Code

I extracted the following code from that output: 

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import os

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Get the file sizes and sort the file paths by size (largest first)
file_sizes = [(file_path, os.path.getsize(file_path)) for file_path in file_paths]
sorted_files = sorted(file_sizes, key=lambda x: x[1], reverse=True)

# Initialize the merged files
merged_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]

# Initialize the current sizes of the merged files
merged_sizes = [0] * 16

# Merge the files and distribute the content evenly
for file_path, file_size in sorted_files:
    # Find the index of the smallest merged file
    smallest_index = merged_sizes.index(min(merged_sizes))

    # Read the content of the file and write it to the smallest merged file
    with open(file_path, "rb") as file:
        content = file.read()
        merged_files[smallest_index].write(content)

    # Update the size of the smallest merged file
    merged_sizes[smallest_index] += file_size

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

Run Code Interpreter

Running the following program: 

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import os

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Get the file sizes and sort the file paths by size (largest first)
file_sizes = [(file_path, os.path.getsize(file_path)) for file_path in file_paths]
sorted_files = sorted(file_sizes, key=lambda x: x[1], reverse=True)

# Initialize the merged files
merged_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]

# Initialize the current sizes of the merged files
merged_sizes = [0] * 16

# Merge the files and distribute the content evenly
for file_path, file_size in sorted_files:
    # Find the index of the smallest merged file
    smallest_index = merged_sizes.index(min(merged_sizes))

    # Read the content of the file and write it to the smallest merged file
    with open(file_path, "rb") as file:
        content = file.read()
        merged_files[smallest_index].write(content)

    # Update the size of the smallest merged file
    merged_sizes[smallest_index] += file_size

# Close the 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 merges the files listed in "/tmp/files", sorts them by size (largest first), and splits the content into 16 files called "/tmp/merged_%d" (where %d is replaced by numbers from 0 to 15), with each file being approximately the same size: 

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import os

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Get the file sizes and sort the file paths 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 the output files
output_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]
output_sizes = [0] * 16

# Merge the files and split the content into 16 files
for path, size in file_sizes:
    # Find the smallest output file
    smallest_index = output_sizes.index(min(output_sizes))

    # Read the content of the file and write it to the smallest output file
    with open(path, "rb") as input_file:
        content = input_file.read()
        output_files[smallest_index].write(content)

    # Update the size of the output file
    output_sizes[smallest_index] += size

# Close the output files
for output_file in output_files:
    output_file.close()
This script does the following: 1. It reads the file paths from "/tmp/files" and stores them in the `file_paths` list. 2. It calculates the size of each file using `os.path.getsize()` and creates a list of tuples `file_sizes`, where each tuple contains the file path and its corresponding size. The `file_sizes` list is then sorted in descending order based on the file sizes. 3. It initializes 16 output files using a list comprehension, creating files with names "/tmp/merged_0" to "/tmp/merged_15". The files are opened in binary write mode (`"wb"`). 4. It initializes a list `output_sizes` to keep track of the current size of each output file. 5. It iterates over the sorted `file_sizes` list. For each file: - It finds the index of the smallest output file by using `output_sizes.index(min(output_sizes))`. - It reads the content of the input file in binary mode. - It writes the content to the smallest output file. - It updates the size of the output file in the `output_sizes` list. 6. Finally, it closes all the output files. This script ensures that the files are merged and split into 16 files of approximately equal size by always adding the next largest file to the smallest output file. Note: Make sure that the "/tmp/files" file contains valid file paths, one per line, and that you have write permissions in the "/tmp" directory.

Extract Code

I extracted the following code from that output: 

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import os

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Get the file sizes and sort the file paths 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 the output files
output_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]
output_sizes = [0] * 16

# Merge the files and split the content into 16 files
for path, size in file_sizes:
    # Find the smallest output file
    smallest_index = output_sizes.index(min(output_sizes))

    # Read the content of the file and write it to the smallest output file
    with open(path, "rb") as input_file:
        content = input_file.read()
        output_files[smallest_index].write(content)

    # Update the size of the output file
    output_sizes[smallest_index] += size

# Close the output files
for output_file in output_files:
    output_file.close()

Run Code Interpreter

Running the following program: 

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import os

# Read the file paths from "/tmp/files"
with open("/tmp/files", "r") as file_list:
    file_paths = file_list.read().splitlines()

# Get the file sizes and sort the file paths 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 the output files
output_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]
output_sizes = [0] * 16

# Merge the files and split the content into 16 files
for path, size in file_sizes:
    # Find the smallest output file
    smallest_index = output_sizes.index(min(output_sizes))

    # Read the content of the file and write it to the smallest output file
    with open(path, "rb") as input_file:
        content = input_file.read()
        output_files[smallest_index].write(content)

    # Update the size of the output file
    output_sizes[smallest_index] += size

# Close the output files
for output_file in output_files:
    output_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 reads the file paths from "/tmp/files", sorts them by size in descending order, and then merges the content into 16 approximately equal-sized files named "/tmp/merged_%d" (where %d is replaced by numbers from 0 to 15): 

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import os

def get_file_size(file_path):
    return os.path.getsize(file_path)

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

# Sort file paths by size in descending order
file_paths.sort(key=get_file_size, reverse=True)

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

# Merge files into 16 approximately equal-sized files
for file_path in file_paths:
    # Find the smallest file among the output files
    smallest_file_index = min(range(num_files), key=lambda i: output_files[i].tell())

    # Read the content of the current file
    with open(file_path, "rb") as input_file:
        content = input_file.read()

    # Write the content to the smallest output file
    output_files[smallest_file_index].write(content)

# Close all output files
for output_file in output_files:
    output_file.close()
Here's how the script works: 1. We define a helper function `get_file_size()` that takes a file path and returns the size of the file using `os.path.getsize()`. 2. We read the file paths from "/tmp/files" into a list called `file_paths`. Each line in the file represents a file path. 3. We sort the `file_paths` list in descending order based on the file sizes using the `sort()` method with the `key` parameter set to `get_file_size` and `reverse=True`. 4. We initialize 16 empty files named "/tmp/merged_%d" using a list comprehension. The files are opened in binary write mode ("wb"). 5. We iterate over each file path in the sorted `file_paths` list. 6. For each file path, we find the smallest file among the output files by comparing their current positions using `output_files[i].tell()`. We use the `min()` function with a lambda function as the `key` to find the index of the smallest file. 7. We read the content of the current file in binary mode and store it in the `content` variable. 8. We write the `content` to the smallest output file using `output_files[smallest_file_index].write(content)`. 9. After processing all the file paths, we close all the output files using a loop. This script will merge the files listed in "/tmp/files" into 16 approximately equal-sized files named "/tmp/merged_0" to "/tmp/merged_15". The files are sorted by size in descending order, and each file is added to the smallest output file to ensure an even distribution of content.

Extract Code

I extracted the following code from that output: 

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import os

def get_file_size(file_path):
    return os.path.getsize(file_path)

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

# Sort file paths by size in descending order
file_paths.sort(key=get_file_size, reverse=True)

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

# Merge files into 16 approximately equal-sized files
for file_path in file_paths:
    # Find the smallest file among the output files
    smallest_file_index = min(range(num_files), key=lambda i: output_files[i].tell())

    # Read the content of the current file
    with open(file_path, "rb") as input_file:
        content = input_file.read()

    # Write the content to the smallest output file
    output_files[smallest_file_index].write(content)

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

Run Code Interpreter

Running the following program: 

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import os

def get_file_size(file_path):
    return os.path.getsize(file_path)

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

# Sort file paths by size in descending order
file_paths.sort(key=get_file_size, reverse=True)

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

# Merge files into 16 approximately equal-sized files
for file_path in file_paths:
    # Find the smallest file among the output files
    smallest_file_index = min(range(num_files), key=lambda i: output_files[i].tell())

    # Read the content of the current file
    with open(file_path, "rb") as input_file:
        content = input_file.read()

    # Write the content to the smallest output file
    output_files[smallest_file_index].write(content)

# Close all output files
for output_file in output_files:
    output_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 described: 

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import os

# Read the file paths from /tmp/files
with open('/tmp/files', 'r') as file:
    file_paths = file.read().splitlines()

# Get the file sizes and sort them 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
num_merged_files = 16
merged_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(num_merged_files)]
merged_file_sizes = [0] * num_merged_files

# Merge the files
for file_path, file_size in file_sizes:
    # Find the smallest merged file
    smallest_index = merged_file_sizes.index(min(merged_file_sizes))

    # Read the content of the file and write it to the smallest merged file
    with open(file_path, 'rb') as file:
        content = file.read()
        merged_files[smallest_index].write(content)

    # Update the size of the smallest merged file
    merged_file_sizes[smallest_index] += file_size

# Close the merged files
for merged_file in merged_files:
    merged_file.close()
Explanation: 1. We start by reading the file paths from `/tmp/files` using `file.read().splitlines()`, which gives us a list of file paths. 2. We calculate the file sizes for each file path using `os.path.getsize()` and create a list of tuples `(file_path, file_size)`. We then sort this list in descending order based on the file sizes using `sort(key=lambda x: x[1], reverse=True)`. 3. We initialize the merged files by creating 16 file objects (`merged_files`) using `open(f'/tmp/merged_{i}', 'wb')` in a list comprehension. We also initialize a list `merged_file_sizes` to keep track of the current size of each merged file. 4. We iterate over the sorted file sizes and their corresponding file paths. For each file: - We find the index of the smallest merged file using `merged_file_sizes.index(min(merged_file_sizes))`. - We read the content of the file using `file.read()` and write it to the smallest merged file using `merged_files[smallest_index].write(content)`. - We update the size of the smallest merged file by adding the file size to `merged_file_sizes[smallest_index]`. 5. Finally, we close all the merged files using a loop. This script will merge the files listed in `/tmp/files` and split the content into 16 files called `/tmp/merged_0`, `/tmp/merged_1`, and so on, up to `/tmp/merged_15`. Each merged file will be approximately the same size, as the script adds files to the smallest merged file at each step.

Extract Code

I extracted the following code from that output: 

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import os

# Read the file paths from /tmp/files
with open('/tmp/files', 'r') as file:
    file_paths = file.read().splitlines()

# Get the file sizes and sort them 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
num_merged_files = 16
merged_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(num_merged_files)]
merged_file_sizes = [0] * num_merged_files

# Merge the files
for file_path, file_size in file_sizes:
    # Find the smallest merged file
    smallest_index = merged_file_sizes.index(min(merged_file_sizes))

    # Read the content of the file and write it to the smallest merged file
    with open(file_path, 'rb') as file:
        content = file.read()
        merged_files[smallest_index].write(content)

    # Update the size of the smallest merged file
    merged_file_sizes[smallest_index] += file_size

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

Run Code Interpreter

Running the following program: 

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import os

# Read the file paths from /tmp/files
with open('/tmp/files', 'r') as file:
    file_paths = file.read().splitlines()

# Get the file sizes and sort them 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
num_merged_files = 16
merged_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(num_merged_files)]
merged_file_sizes = [0] * num_merged_files

# Merge the files
for file_path, file_size in file_sizes:
    # Find the smallest merged file
    smallest_index = merged_file_sizes.index(min(merged_file_sizes))

    # Read the content of the file and write it to the smallest merged file
    with open(file_path, 'rb') as file:
        content = file.read()
        merged_files[smallest_index].write(content)

    # Update the size of the smallest merged file
    merged_file_sizes[smallest_index] += file_size

# Close the 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 reads the file paths from `/tmp/files`, merges the content of the files, and splits the merged content into 16 approximately equal-sized files: 

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import os

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

# Get file sizes and 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 output files
num_output_files = 16
output_files = [open(f'/tmp/merged_{i}', 'w') for i in range(num_output_files)]
output_sizes = [0] * num_output_files

# Merge files and split content
for path, size in file_sizes:
    # Find the smallest output file
    smallest_index = output_sizes.index(min(output_sizes))

    # Read content from the input file and write to the smallest output file
    with open(path, 'r') as input_file:
        content = input_file.read()
        output_files[smallest_index].write(content)
        output_sizes[smallest_index] += size

# Close output files
for output_file in output_files:
    output_file.close()
Explanation: 1. We start by reading the file paths from `/tmp/files` and storing them in the `file_paths` list. 2. We calculate the size of each file using `os.path.getsize()` and create a list of tuples `file_sizes`, where each tuple contains the file path and its corresponding size. We then sort `file_sizes` in descending order based on the file sizes. 3. We initialize 16 output files named `/tmp/merged_0`, `/tmp/merged_1`, and so on, up to `/tmp/merged_15`. We also create a list `output_sizes` to keep track of the current size of each output file. 4. We iterate over the sorted `file_sizes` list. For each file: - We find the index of the smallest output file by using `output_sizes.index(min(output_sizes))`. - We read the content of the input file and write it to the smallest output file. - We update the size of the corresponding output file in `output_sizes`. 5. Finally, we close all the output files. This script will merge the content of the files listed in `/tmp/files` and split the merged content into 16 approximately equal-sized files named `/tmp/merged_0`, `/tmp/merged_1`, and so on, up to `/tmp/merged_15`. The files will be sorted by size (largest first) and added to the smallest output file each time to ensure an even distribution of content.

Extract Code

I extracted the following code from that output: 

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import os

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

# Get file sizes and 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 output files
num_output_files = 16
output_files = [open(f'/tmp/merged_{i}', 'w') for i in range(num_output_files)]
output_sizes = [0] * num_output_files

# Merge files and split content
for path, size in file_sizes:
    # Find the smallest output file
    smallest_index = output_sizes.index(min(output_sizes))

    # Read content from the input file and write to the smallest output file
    with open(path, 'r') as input_file:
        content = input_file.read()
        output_files[smallest_index].write(content)
        output_sizes[smallest_index] += size

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

Run Code Interpreter

Running the following program: 

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import os

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

# Get file sizes and 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 output files
num_output_files = 16
output_files = [open(f'/tmp/merged_{i}', 'w') for i in range(num_output_files)]
output_sizes = [0] * num_output_files

# Merge files and split content
for path, size in file_sizes:
    # Find the smallest output file
    smallest_index = output_sizes.index(min(output_sizes))

    # Read content from the input file and write to the smallest output file
    with open(path, 'r') as input_file:
        content = input_file.read()
        output_files[smallest_index].write(content)
        output_sizes[smallest_index] += size

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

PyFunc

final: True Resulting in output: True