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 reads file paths from `/tmp/files` and merges their content into 16 files in `/tmp/merged_%d`: 

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import os

input_file = "/tmp/files"
output_dir = "/tmp/merged_"
num_output_files = 16

with open(input_file, "r") as f:
    file_paths = f.readlines()
file_paths = [path.strip() for path in file_paths]

file_iter = iter(file_paths)
current_output_file_idx = 0
current_output_file_size = 0
current_output_file = None

for file_path in file_iter:
    with open(file_path, "rb") as f:
        file_content = f.read()
        file_size = len(file_content)

        if current_output_file is None or current_output_file_size + file_size > (len(file_paths) / num_output_files) * 1024 * 1024:
            # Close the current output file if it exists or if it will exceed the target size
            if current_output_file is not None:
                current_output_file.close()

            # Open a new output file
            current_output_file_idx += 1
            current_output_file_name = output_dir + str(current_output_file_idx).zfill(2)
            current_output_file = open(current_output_file_name, "wb")
            current_output_file_size = 0

        # Write the content to the current output file
        current_output_file.write(file_content)
        current_output_file_size += file_size

# Close the last output file if it's still open
if current_output_file is not None:
    current_output_file.close()
This script reads the input file paths from `/tmp/files`, iterates through them, and writes their content to the appropriate output file based on the target file size (approximately equal size for each of the 16 files). Note that the target size is calculated based on the total number of input files and assumes an average file size. Make sure to replace the `input_file` and `output_dir` variables with the correct paths if needed. The script also assumes that the input files are in the same format and can be merged without issues.

Extract Code

I extracted the following code from that output: 

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import os

input_file = "/tmp/files"
output_dir = "/tmp/merged_"
num_output_files = 16

with open(input_file, "r") as f:
    file_paths = f.readlines()
file_paths = [path.strip() for path in file_paths]

file_iter = iter(file_paths)
current_output_file_idx = 0
current_output_file_size = 0
current_output_file = None

for file_path in file_iter:
    with open(file_path, "rb") as f:
        file_content = f.read()
        file_size = len(file_content)

        if current_output_file is None or current_output_file_size + file_size > (len(file_paths) / num_output_files) * 1024 * 1024:
            # Close the current output file if it exists or if it will exceed the target size
            if current_output_file is not None:
                current_output_file.close()

            # Open a new output file
            current_output_file_idx += 1
            current_output_file_name = output_dir + str(current_output_file_idx).zfill(2)
            current_output_file = open(current_output_file_name, "wb")
            current_output_file_size = 0

        # Write the content to the current output file
        current_output_file.write(file_content)
        current_output_file_size += file_size

# Close the last output file if it's still open
if current_output_file is not None:
    current_output_file.close()

Run Code Interpreter

Running the following program: 

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import os

input_file = "/tmp/files"
output_dir = "/tmp/merged_"
num_output_files = 16

with open(input_file, "r") as f:
    file_paths = f.readlines()
file_paths = [path.strip() for path in file_paths]

file_iter = iter(file_paths)
current_output_file_idx = 0
current_output_file_size = 0
current_output_file = None

for file_path in file_iter:
    with open(file_path, "rb") as f:
        file_content = f.read()
        file_size = len(file_content)

        if current_output_file is None or current_output_file_size + file_size > (len(file_paths) / num_output_files) * 1024 * 1024:
            # Close the current output file if it exists or if it will exceed the target size
            if current_output_file is not None:
                current_output_file.close()

            # Open a new output file
            current_output_file_idx += 1
            current_output_file_name = output_dir + str(current_output_file_idx).zfill(2)
            current_output_file = open(current_output_file_name, "wb")
            current_output_file_size = 0

        # Write the content to the current output file
        current_output_file.write(file_content)
        current_output_file_size += file_size

# Close the last output file if it's still open
if current_output_file is not None:
    current_output_file.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 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 should do what you're asking for: 

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import os

# Set the input file path and the output directory path
input_file_path = "/tmp/files"
output_dir_path = "/tmp/merged_"

# Initialize the file counter and the current file size
file_counter = 0
current_file_size = 0

# Open the input file and read the list of file paths
with open(input_file_path, "r") as f:
  file_paths = [line.strip() for line in f]

# Calculate the number of output files needed
num_output_files = (len(file_paths) + 15) // 16

# Iterate through the list of file paths and write the contents to the output files
for i, file_path in enumerate(file_paths):
  # Open the current output file
  output_file_path = os.path.join(output_dir_path, str(file_counter))
  with open(output_file_path, "ab") as f:
    # Open the current input file and read its contents
    with open(file_path, "rb") as g:
      contents = g.read()
      # Write the contents to the output file and update the current file size
      f.write(contents)
      current_file_size += len(contents)
    # If the current output file is full, or if we've reached the end of the input file list,
    # close the current output file and increment the file counter
    if current_file_size >= 1048576 or i == len(file_paths) - 1:
      current_file_size = 0
      file_counter += 1
This script reads the list of file paths from the `/tmp/files` file, and then iterates through the list and writes the contents of each file to one of the output files in the `/tmp/merged_` directory. It uses binary mode for reading and writing the files, and it tries to make each output file approximately the same size by checking the current file size after each file is written. If the current file size is greater than or equal to 1 MB, or if it's the last file in the input list, it closes the current output file and starts a new one. Note that this script assumes that the total size of all the input files is less than 16 MB. If the total size is greater than 16 MB, the last output file will be larger than the others. You can adjust the file size limit by changing the value of the `current_file_size` variable in the script. I hope this helps! Let me know if you have any questions. ### **Edit:** Here's a revised version of the script that uses the built-in `os.path.getsize()` function to get the size of each file before writing it to the output file. This allows the script to more accurately distribute the files among the output files: 
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import os

# Set the input file path and the output directory path
input_file_path = "/tmp/files"
output_dir_path = "/tmp/merged_"

# Initialize the file counter and the current file size
file_counter = 0
current_file_size = 0

# Open the input file and read the list of file paths
with open(input_file_path, "r") as f:
  file_paths = [line.strip() for line in f]

# Calculate the number of output files needed
num_output_files = (len(file_paths) + 15) // 16

# Iterate through the list of file paths and write the contents to the output files
for i, file_path in enumerate(file_paths):
  # Get the size of the current input file
  file_size = os.path.getsize(file_path)
  # If the current output file is full, or if we've reached the end of the input file list,
  # close the current output file and increment the file counter
  if current_file_size + file_size >= 1048576 or i == len(file_paths) - 1:
    current_file_size = 0
    file_counter += 1
  # Open the current output file
  output_file_path = os.path.join(output_dir_path, str(file_counter))
  with open(output_file_path, "ab") as f:
    # Open the current input file and read its contents
    with open(file_path, "rb") as g:
      contents = g.read()
      # Write the contents to the output file and update the current file size
      f.write(contents)
      current_file_size += len(contents)
This revised script works the same way as the original script, but it uses the `os.path.getsize()` function to get the size of each input file before writing it to the output file. This allows the script to more accurately distribute the files among the output files, since it can now take into account the size of each file before deciding which output file to write it to. I hope this helps! Let me know if you have any questions. ### **Edit 2:** Here's a further revised version of the script that uses the `os.path.exists()` function to check if the output files already exist before writing to them. This allows the script to skip over any output files that have already been created, and it also allows the script to resume where it left off if it's interrupted and restarted: 
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import os

# Set the input file path and the output directory path
input_file_path = "/tmp/files"
output_dir_path = "/tmp/merged_"

# Initialize the file counter and the current file size
file_counter = 0
current_file_size = 0

# Open the input file and read the list of file paths
with open(input_file_path, "r") as f:
  file_paths = [line.strip() for line in f]

# Calculate the number of output files needed
num_output_files = (len(file_paths) + 15) // 16

# Iterate through the list of file paths and write the contents to the output files
for i, file_path in enumerate(file_paths):
  # Get the size of the current input file
  file_size = os.path.getsize(file_path)
  # If the current output file is full, or if we've reached the end of the input file list,
  # close the current output file and increment the file counter
  while current_file_size + file_size >= 1048576 or i == len(file_paths) - 1:
    current_file_size = 0
    file_counter += 1
  # If the current output file doesn't exist yet, create it
  output_file_path = os.path.join(output_dir_path, str(file_counter))
  if not os.path.exists(output_file_path):
    open(output_file_path, "wb").close()
  # Open the current output file
  with open(output_file_path, "ab") as f:
    # Open the current input file and read its contents
    with open(file_path, "rb") as g:
      contents = g.read()
      # Write the contents to the output file and update the current file size
      f.write(contents)
      current_file_size += len(contents)
This revised script works the same way as the previous version, but it uses the `os.path.exists()` function to check if each output file already exists before writing to it. If the output file doesn't exist, it creates it using the `open()` function with the `"wb"` mode. This allows the script to skip over any output files that have already been created, and it also allows the script to resume where it left off if it's interrupted and restarted. I hope this helps! Let me know if you have any questions. ### **Edit 3:** Here's a final version of the script that includes error handling for the case where the input file doesn't exist or can't be read, or where the output files can't be written: 
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import os

# Set the input file path and the output directory path
input_file_path = "/tmp/files"
output_dir_path = "/tmp/merged_"

# Initialize the file counter and the current file size
file_counter = 0
current_file_size = 0

try:
  # Open the input file and read the list of file paths
  with

Extract Code

I extracted the following code from that output: 

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import os

# Set the input file path and the output directory path
input_file_path = "/tmp/files"
output_dir_path = "/tmp/merged_"

# Initialize the file counter and the current file size
file_counter = 0
current_file_size = 0

try:
  # Open the input file and read the list of file paths
  with open(input_file_path, "r") as f:
    file_paths = [line.strip() for line in f]
except FileNotFoundError:
  print(f"Error: The input file '{input_file_path}' does not exist or cannot be read.")
  exit()

# Calculate the number of output files needed
num_output_files = (len(file_paths) + 15) // 16

# Iterate through the list of file paths and write the contents to the output files
for i, file_path in enumerate(file_paths):
  # Get the size of the current input file
  file_size = os.path.getsize(file_path)
  # If the current output file is full, or if we've reached the end of the input file list,
  # close the current output file and increment the file counter
  while current_file_size + file_size >= 1048576 or i == len(file_paths) - 1:
    current_file_size = 0
    file_counter += 1
  # If the current output file doesn't exist yet, create it
  output_file_path = os.path.join(output_dir_path, str(file_counter))
  if not os.path.exists(output_file_path):
    open(output_file_path, "wb").close()
  # Open the current output file
  try:
    with open(output_file_path, "ab") as f:
      # Open the current input file and read its contents
      with open(file_path, "rb") as g:
        contents = g.read()
        # Write the contents to the output file and update the current file size
        f.write(contents)
        current_file_size += len(contents)
  except IOError:
    print(f"Error: Unable to write to the output file '{output_file_path}'.")
    exit()

Run Code Interpreter

Running the following program: 

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import os

# Set the input file path and the output directory path
input_file_path = "/tmp/files"
output_dir_path = "/tmp/merged_"

# Initialize the file counter and the current file size
file_counter = 0
current_file_size = 0

try:
  # Open the input file and read the list of file paths
  with open(input_file_path, "r") as f:
    file_paths = [line.strip() for line in f]
except FileNotFoundError:
  print(f"Error: The input file '{input_file_path}' does not exist or cannot be read.")
  exit()

# Calculate the number of output files needed
num_output_files = (len(file_paths) + 15) // 16

# Iterate through the list of file paths and write the contents to the output files
for i, file_path in enumerate(file_paths):
  # Get the size of the current input file
  file_size = os.path.getsize(file_path)
  # If the current output file is full, or if we've reached the end of the input file list,
  # close the current output file and increment the file counter
  while current_file_size + file_size >= 1048576 or i == len(file_paths) - 1:
    current_file_size = 0
    file_counter += 1
  # If the current output file doesn't exist yet, create it
  output_file_path = os.path.join(output_dir_path, str(file_counter))
  if not os.path.exists(output_file_path):
    open(output_file_path, "wb").close()
  # Open the current output file
  try:
    with open(output_file_path, "ab") as f:
      # Open the current input file and read its contents
      with open(file_path, "rb") as g:
        contents = g.read()
        # Write the contents to the output file and update the current file size
        f.write(contents)
        current_file_size += len(contents)
  except IOError:
    print(f"Error: Unable to write to the output file '{output_file_path}'.")
    exit()
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 35, in <module> open(output_file_path, "wb").close() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/tmp/merged_/0'

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 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 should do what you're asking for: 

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# Read the list of file paths from /tmp/files
with open('/tmp/files', 'r') as f:
    file_paths = f.readlines()

# Strip newline characters from the file paths
file_paths = [path.strip() for path in file_paths]

# Calculate the number of files to merge and the size of each merged file
num_files = 16
total_size = sum([os.path.getsize(path) for path in file_paths])
avg_size = total_size // num_files

# Initialize a list to store the file paths for each merged file
merged_files = [[] for _ in range(num_files)]

# Initialize a variable to keep track of the current merged file index
cur_index = 0

# Loop through the file paths and assign them to the appropriate merged file
for path in file_paths:
    size = os.path.getsize(path)
    if sum([len(merged_files[i]) * avg_size for i in range(cur_index)]) + size > total_size:
        # If adding the current file would cause the merged file to exceed the average size,
        # move to the next merged file
        cur_index = (cur_index + 1) % num_files

    merged_files[cur_index].append(path)

# Write the contents of each merged file
for i, paths in enumerate(merged_files):
    with open(f'/tmp/merged_{i}', 'w') as f:
        for path in paths:
            with open(path, 'r') as g:
                f.write(g.read())
This script first reads the list of file paths from `/tmp/files` and calculates the total size of all the files. It then calculates the average size of each merged file and initializes a list to store the file paths for each merged file. The script then loops through the file paths and assigns each one to a merged file based on its size. If adding a file to the current merged file would cause it to exceed the average size, the script moves to the next merged file. Finally, the script writes the contents of each merged file to disk. Note that this script assumes that the file paths in `/tmp/files` are all relative to the current working directory. If the file paths are absolute, you'll need to modify the script accordingly. Also note that this script doesn't handle the case where the total size of the files isn't evenly divisible by the number of merged files. In that case, some of the merged files may be slightly larger or smaller than the average size. If you want to ensure that all the merged files are exactly the same size, you can modify the script to handle this case.

Extract Code

I extracted the following code from that output: 

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# Read the list of file paths from /tmp/files
with open('/tmp/files', 'r') as f:
    file_paths = f.readlines()

# Strip newline characters from the file paths
file_paths = [path.strip() for path in file_paths]

# Calculate the number of files to merge and the size of each merged file
num_files = 16
total_size = sum([os.path.getsize(path) for path in file_paths])
avg_size = total_size // num_files

# Initialize a list to store the file paths for each merged file
merged_files = [[] for _ in range(num_files)]

# Initialize a variable to keep track of the current merged file index
cur_index = 0

# Loop through the file paths and assign them to the appropriate merged file
for path in file_paths:
    size = os.path.getsize(path)
    if sum([len(merged_files[i]) * avg_size for i in range(cur_index)]) + size > total_size:
        # If adding the current file would cause the merged file to exceed the average size,
        # move to the next merged file
        cur_index = (cur_index + 1) % num_files

    merged_files[cur_index].append(path)

# Write the contents of each merged file
for i, paths in enumerate(merged_files):
    with open(f'/tmp/merged_{i}', 'w') as f:
        for path in paths:
            with open(path, 'r') as g:
                f.write(g.read())

Run Code Interpreter

Running the following program: 

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# Read the list of file paths from /tmp/files
with open('/tmp/files', 'r') as f:
    file_paths = f.readlines()

# Strip newline characters from the file paths
file_paths = [path.strip() for path in file_paths]

# Calculate the number of files to merge and the size of each merged file
num_files = 16
total_size = sum([os.path.getsize(path) for path in file_paths])
avg_size = total_size // num_files

# Initialize a list to store the file paths for each merged file
merged_files = [[] for _ in range(num_files)]

# Initialize a variable to keep track of the current merged file index
cur_index = 0

# Loop through the file paths and assign them to the appropriate merged file
for path in file_paths:
    size = os.path.getsize(path)
    if sum([len(merged_files[i]) * avg_size for i in range(cur_index)]) + size > total_size:
        # If adding the current file would cause the merged file to exceed the average size,
        # move to the next merged file
        cur_index = (cur_index + 1) % num_files

    merged_files[cur_index].append(path)

# Write the contents of each merged file
for i, paths in enumerate(merged_files):
    with open(f'/tmp/merged_{i}', 'w') as f:
        for path in paths:
            with open(path, 'r') as g:
                f.write(g.read())
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 11, in <module> total_size = sum([os.path.getsize(path) for path in file_paths]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 11, in <listcomp> total_size = sum([os.path.getsize(path) for path in file_paths]) ^^ NameError: name 'os' is not defined

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 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 should do what you're asking for: 

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import os

# Set the input and output directories
input_dir = "/tmp"
output_dir = "/tmp"

# Set the number of output files to create
num_output_files = 16

# Calculate the size of each output file based on the total size of all input files
input_files_path = os.path.join(input_dir, "files")
with open(input_files_path, "r") as f:
    input_files = f.readlines()
input_files = [os.path.join(input_dir, x.strip()) for x in input_files]
total_size = sum(os.path.getsize(f) for f in input_files)
output_size = total_size // num_output_files

# Initialize variables to keep track of the current output file and its size
cur_output_file = 0
cur_output_size = 0

# Open the first output file
cur_output_path = os.path.join(output_dir, "merged_{}".format(cur_output_file))
with open(cur_output_path, "wb") as cur_output:
    # Iterate through the input files
    for i, input_file in enumerate(input_files):
        # Open the current input file
        with open(input_file, "rb") as f:
            # Read the file content in chunks to avoid loading the entire file into memory
            while True:
                # Read a chunk of the file
                chunk = f.read(4096)
                # If the chunk is empty, the file has been read completely
                if not chunk:
                    break
                # Check if the current output file still has room for the chunk
                if cur_output_size + len(chunk) > output_size:
                    # Close the current output file
                    cur_output.close()
                    # Open the next output file
                    cur_output_file += 1
                    cur_output_path = os.path.join(output_dir, "merged_{}".format(cur_output_file))
                    cur_output = open(cur_output_path, "wb")
                    cur_output_size = 0
                # Write the chunk to the current output file
                cur_output.write(chunk)
                cur_output_size += len(chunk)
# Close the last output file
cur_output.close()
This script will read the file paths from `/tmp/files` and merge their contents into 16 files called `/tmp/merged_0` through `/tmp/merged_15`, with each file being approximately the same size. Note that the script uses a buffer size of 4096 bytes to read the input files in chunks, in order to avoid loading the entire file into memory. You can adjust this buffer size as needed. Also note that the script does not handle cases where the total size of the input files is not evenly divisible by 16. In such cases, the last output file may be smaller than the others. If you need to handle this case differently, you can modify the script accordingly.

Extract Code

I extracted the following code from that output: 

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import os

# Set the input and output directories
input_dir = "/tmp"
output_dir = "/tmp"

# Set the number of output files to create
num_output_files = 16

# Calculate the size of each output file based on the total size of all input files
input_files_path = os.path.join(input_dir, "files")
with open(input_files_path, "r") as f:
    input_files = f.readlines()
input_files = [os.path.join(input_dir, x.strip()) for x in input_files]
total_size = sum(os.path.getsize(f) for f in input_files)
output_size = total_size // num_output_files

# Initialize variables to keep track of the current output file and its size
cur_output_file = 0
cur_output_size = 0

# Open the first output file
cur_output_path = os.path.join(output_dir, "merged_{}".format(cur_output_file))
with open(cur_output_path, "wb") as cur_output:
    # Iterate through the input files
    for i, input_file in enumerate(input_files):
        # Open the current input file
        with open(input_file, "rb") as f:
            # Read the file content in chunks to avoid loading the entire file into memory
            while True:
                # Read a chunk of the file
                chunk = f.read(4096)
                # If the chunk is empty, the file has been read completely
                if not chunk:
                    break
                # Check if the current output file still has room for the chunk
                if cur_output_size + len(chunk) > output_size:
                    # Close the current output file
                    cur_output.close()
                    # Open the next output file
                    cur_output_file += 1
                    cur_output_path = os.path.join(output_dir, "merged_{}".format(cur_output_file))
                    cur_output = open(cur_output_path, "wb")
                    cur_output_size = 0
                # Write the chunk to the current output file
                cur_output.write(chunk)
                cur_output_size += len(chunk)
# Close the last output file
cur_output.close()

Run Code Interpreter

Running the following program: 

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import os

# Set the input and output directories
input_dir = "/tmp"
output_dir = "/tmp"

# Set the number of output files to create
num_output_files = 16

# Calculate the size of each output file based on the total size of all input files
input_files_path = os.path.join(input_dir, "files")
with open(input_files_path, "r") as f:
    input_files = f.readlines()
input_files = [os.path.join(input_dir, x.strip()) for x in input_files]
total_size = sum(os.path.getsize(f) for f in input_files)
output_size = total_size // num_output_files

# Initialize variables to keep track of the current output file and its size
cur_output_file = 0
cur_output_size = 0

# Open the first output file
cur_output_path = os.path.join(output_dir, "merged_{}".format(cur_output_file))
with open(cur_output_path, "wb") as cur_output:
    # Iterate through the input files
    for i, input_file in enumerate(input_files):
        # Open the current input file
        with open(input_file, "rb") as f:
            # Read the file content in chunks to avoid loading the entire file into memory
            while True:
                # Read a chunk of the file
                chunk = f.read(4096)
                # If the chunk is empty, the file has been read completely
                if not chunk:
                    break
                # Check if the current output file still has room for the chunk
                if cur_output_size + len(chunk) > output_size:
                    # Close the current output file
                    cur_output.close()
                    # Open the next output file
                    cur_output_file += 1
                    cur_output_path = os.path.join(output_dir, "merged_{}".format(cur_output_file))
                    cur_output = open(cur_output_path, "wb")
                    cur_output_size = 0
                # Write the chunk to the current output file
                cur_output.write(chunk)
                cur_output_size += len(chunk)
# Close the last output file
cur_output.close()
And got the output:

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 should do what you're asking for: 

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

# Calculate the number of files to merge
num_files = 16

# Initialize an array to hold the merged files
merged_files = [[] for _ in range(num_files)]

# Evenly distribute the file paths across the merged files
for i, file_path in enumerate(file_paths):
    merged_files[i % num_files].append(file_path)

# Merge the content of each group of files
for i, file_paths in enumerate(merged_files):
    with open(f'/tmp/merged_{i}', 'w') as merged_file:
        for file_path in file_paths:
            with open(file_path, 'r') as file:
                merged_file.write(file.read())
This script first reads the list of file paths from `/tmp/files` and stores them in a list called `file_paths`. It then calculates the number of merged files, which is 16 in this case, and initializes an array called `merged_files` to hold the file paths for each merged file. Next, the script iterates over the `file_paths` list and evenly distributes the file paths across the `merged_files` array using the modulo operator (`%`). This ensures that each merged file contains approximately the same number of files. Finally, the script iterates over the `merged_files` array and merges the content of each group of files into a single file. The resulting merged files will be named `/tmp/merged_0`, `/tmp/merged_1`, ..., `/tmp/merged_15`. Note that this script does not guarantee that the merged files will be exactly the same size, since the size of the original files may vary. However, it should distribute the files evenly across the merged files so that they are approximately the same size. I hope this helps! Let me know if you have any questions or if you'd like to see any modifications to the script.

Extract Code

I extracted the following code from that output: 

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

# Calculate the number of files to merge
num_files = 16

# Initialize an array to hold the merged files
merged_files = [[] for _ in range(num_files)]

# Evenly distribute the file paths across the merged files
for i, file_path in enumerate(file_paths):
    merged_files[i % num_files].append(file_path)

# Merge the content of each group of files
for i, file_paths in enumerate(merged_files):
    with open(f'/tmp/merged_{i}', 'w') as merged_file:
        for file_path in file_paths:
            with open(file_path, 'r') as file:
                merged_file.write(file.read())

Run Code Interpreter

Running the following program: 

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

# Calculate the number of files to merge
num_files = 16

# Initialize an array to hold the merged files
merged_files = [[] for _ in range(num_files)]

# Evenly distribute the file paths across the merged files
for i, file_path in enumerate(file_paths):
    merged_files[i % num_files].append(file_path)

# Merge the content of each group of files
for i, file_paths in enumerate(merged_files):
    with open(f'/tmp/merged_{i}', 'w') as merged_file:
        for file_path in file_paths:
            with open(file_path, 'r') as file:
                merged_file.write(file.read())
And got the output:

PyFunc

final: False Resulting in output: False