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 need: 

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import os
import math

# Open the file containing the list of file paths
with open('/tmp/files', 'r') as file_paths:
    # Read all the file paths into a list
    all_files = file_paths.read().splitlines()

# Calculate the number of files to merge into each output file
num_files = len(all_files)
files_per_output = math.ceil(num_files / 16)

# Iterate through the list of file paths and merge them into output files
for i in range(16):
    output_file = f'/tmp/merged_{i}'
    start_index = i * files_per_output
    end_index = min((i + 1) * files_per_output, num_files)
    files_to_merge = all_files[start_index:end_index]

    with open(output_file, 'wb') as merged_file:
        for file_path in files_to_merge:
            with open(file_path, 'rb') as input_file:
                merged_file.write(input_file.read())
Here's how the script works: 1. The script opens the `/tmp/files` file and reads all the file paths into a list called `all_files`. 2. It calculates the number of files to merge into each output file by dividing the total number of files by 16 and rounding up using `math.ceil`. 3. The script then iterates through a range of 16 numbers (0 to 15), representing the 16 output files to be created. 4. For each iteration: - It constructs the output file name using the format `/tmp/merged_{i}`, where `i` is the current iteration number. - It calculates the start and end indices of the files to be merged into the current output file based on the `files_per_output` value. - It creates a sublist `files_to_merge` containing the file paths to be merged into the current output file. - The script opens the output file for writing in binary mode. - For each file path in `files_to_merge`, it opens the corresponding file in binary mode and writes its contents to the output file. After running this script, you should have 16 files named `/tmp/merged_0` to `/tmp/merged_15` containing the merged contents of the files listed in `/tmp/files`. Each output file should contain approximately the same amount of data, with the last file potentially being slightly smaller if the total number of files is not evenly divisible by 16.

Extract Code

I extracted the following code from that output: 

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import os
import math

# Open the file containing the list of file paths
with open('/tmp/files', 'r') as file_paths:
    # Read all the file paths into a list
    all_files = file_paths.read().splitlines()

# Calculate the number of files to merge into each output file
num_files = len(all_files)
files_per_output = math.ceil(num_files / 16)

# Iterate through the list of file paths and merge them into output files
for i in range(16):
    output_file = f'/tmp/merged_{i}'
    start_index = i * files_per_output
    end_index = min((i + 1) * files_per_output, num_files)
    files_to_merge = all_files[start_index:end_index]

    with open(output_file, 'wb') as merged_file:
        for file_path in files_to_merge:
            with open(file_path, 'rb') as input_file:
                merged_file.write(input_file.read())

Run Code Interpreter

Running the following program: 

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import os
import math

# Open the file containing the list of file paths
with open('/tmp/files', 'r') as file_paths:
    # Read all the file paths into a list
    all_files = file_paths.read().splitlines()

# Calculate the number of files to merge into each output file
num_files = len(all_files)
files_per_output = math.ceil(num_files / 16)

# Iterate through the list of file paths and merge them into output files
for i in range(16):
    output_file = f'/tmp/merged_{i}'
    start_index = i * files_per_output
    end_index = min((i + 1) * files_per_output, num_files)
    files_to_merge = all_files[start_index:end_index]

    with open(output_file, 'wb') as merged_file:
        for file_path in files_to_merge:
            with open(file_path, 'rb') as input_file:
                merged_file.write(input_file.read())
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 will merge the contents of the files listed in `/tmp/files` into 16 files called `/tmp/merged_0`, `/tmp/merged_1`, ..., `/tmp/merged_15`. Each merged file will have approximately the same size: 

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import os
import math

# Path to the file containing the list of file paths
file_list_path = "/tmp/files"

# Path prefix for the merged files
merged_file_prefix = "/tmp/merged_"

# Number of merged files to create
num_merged_files = 16

# Read the list of file paths
with open(file_list_path, "r") as file:
    file_paths = file.read().splitlines()

# Calculate the size of each merged file
total_size = sum(os.path.getsize(path) for path in file_paths)
target_merged_file_size = math.ceil(total_size / num_merged_files)

# Initialize the current merged file index and size
current_merged_file_index = 0
current_merged_file_size = 0

# Open the first merged file for writing
current_merged_file = open(f"{merged_file_prefix}{current_merged_file_index}", "wb")

# Iterate over the file paths
for file_path in file_paths:
    # Get the size of the current file
    file_size = os.path.getsize(file_path)

    # If adding the current file would exceed the target size for the current merged file,
    # close the current merged file and open the next one
    if current_merged_file_size + file_size > target_merged_file_size:
        current_merged_file.close()
        current_merged_file_index += 1
        current_merged_file_size = 0
        current_merged_file = open(f"{merged_file_prefix}{current_merged_file_index}", "wb")

    # Append the contents of the current file to the current merged file
    with open(file_path, "rb") as file:
        content = file.read()
        current_merged_file.write(content)

    # Update the size of the current merged file
    current_merged_file_size += file_size

# Close the last merged file
current_merged_file.close()
Here's how the script works: 1. It reads the list of file paths from `/tmp/files`. 2. It calculates the total size of all files and determines the target size for each merged file based on the desired number of merged files (16 in this case). 3. It initializes a variable to keep track of the current merged file index and size. 4. For each file path in the list, it checks if adding the current file would exceed the target size for the current merged file. If so, it closes the current merged file and opens a new one. 5. It appends the contents of the current file to the current merged file and updates the size of the current merged file. 6. After processing all files, it closes the last merged file. Note that this script assumes that the file paths are valid and accessible. It also assumes that there's enough disk space available to create the merged files. If you need to handle errors or optimize the script for large files, you may need to make additional modifications.

Extract Code

I extracted the following code from that output: 

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import os
import math

# Path to the file containing the list of file paths
file_list_path = "/tmp/files"

# Path prefix for the merged files
merged_file_prefix = "/tmp/merged_"

# Number of merged files to create
num_merged_files = 16

# Read the list of file paths
with open(file_list_path, "r") as file:
    file_paths = file.read().splitlines()

# Calculate the size of each merged file
total_size = sum(os.path.getsize(path) for path in file_paths)
target_merged_file_size = math.ceil(total_size / num_merged_files)

# Initialize the current merged file index and size
current_merged_file_index = 0
current_merged_file_size = 0

# Open the first merged file for writing
current_merged_file = open(f"{merged_file_prefix}{current_merged_file_index}", "wb")

# Iterate over the file paths
for file_path in file_paths:
    # Get the size of the current file
    file_size = os.path.getsize(file_path)

    # If adding the current file would exceed the target size for the current merged file,
    # close the current merged file and open the next one
    if current_merged_file_size + file_size > target_merged_file_size:
        current_merged_file.close()
        current_merged_file_index += 1
        current_merged_file_size = 0
        current_merged_file = open(f"{merged_file_prefix}{current_merged_file_index}", "wb")

    # Append the contents of the current file to the current merged file
    with open(file_path, "rb") as file:
        content = file.read()
        current_merged_file.write(content)

    # Update the size of the current merged file
    current_merged_file_size += file_size

# Close the last merged file
current_merged_file.close()

Run Code Interpreter

Running the following program: 

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import os
import math

# Path to the file containing the list of file paths
file_list_path = "/tmp/files"

# Path prefix for the merged files
merged_file_prefix = "/tmp/merged_"

# Number of merged files to create
num_merged_files = 16

# Read the list of file paths
with open(file_list_path, "r") as file:
    file_paths = file.read().splitlines()

# Calculate the size of each merged file
total_size = sum(os.path.getsize(path) for path in file_paths)
target_merged_file_size = math.ceil(total_size / num_merged_files)

# Initialize the current merged file index and size
current_merged_file_index = 0
current_merged_file_size = 0

# Open the first merged file for writing
current_merged_file = open(f"{merged_file_prefix}{current_merged_file_index}", "wb")

# Iterate over the file paths
for file_path in file_paths:
    # Get the size of the current file
    file_size = os.path.getsize(file_path)

    # If adding the current file would exceed the target size for the current merged file,
    # close the current merged file and open the next one
    if current_merged_file_size + file_size > target_merged_file_size:
        current_merged_file.close()
        current_merged_file_index += 1
        current_merged_file_size = 0
        current_merged_file = open(f"{merged_file_prefix}{current_merged_file_index}", "wb")

    # Append the contents of the current file to the current merged file
    with open(file_path, "rb") as file:
        content = file.read()
        current_merged_file.write(content)

    # Update the size of the current merged file
    current_merged_file_size += file_size

# Close the last merged file
current_merged_file.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 achieve what you're looking for: 

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import os
from math import ceil

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

# Calculate the number of files per merged file
num_files_per_merged = ceil(len(file_paths) / 16)

# Iterate over the desired merged files
for i in range(16):
    # Open the merged file for writing
    merged_file_path = f'/tmp/merged_{i}'
    with open(merged_file_path, 'w') as merged_file:
        # Determine the start and end indices for the current batch of files
        start_idx = i * num_files_per_merged
        end_idx = min((i + 1) * num_files_per_merged, len(file_paths))

        # Process each file in the current batch
        for file_path in file_paths[start_idx:end_idx]:
            try:
                # Open the file for reading
                with open(file_path, 'r') as f:
                    # Write the contents to the merged file
                    merged_file.write(f.read())
            except Exception as e:
                print(f'Error reading file {file_path}: {e}')

print('Merging complete.')
Here's how the script works: 1. The file paths are read from `/tmp/files` and stored in the `file_paths` list. 2. The `num_files_per_merged` variable is calculated by dividing the total number of files by 16 and rounding up using `ceil`. 3. The script then iterates over the desired 16 merged files. 4. For each merged file, the start and end indices for the current batch of files are determined based on the `num_files_per_merged` value. 5. The script then processes each file in the current batch by opening it for reading and writing its contents to the corresponding merged file. 6. If an error occurs while reading a file, the error is printed to the console. 7. After all files have been processed, a message is printed to indicate that the merging is complete. Note that this script assumes that all files can be read without issue. If you need to handle specific types of errors or have other requirements, you may need to modify the script accordingly.

Extract Code

I extracted the following code from that output: 

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import os
from math import ceil

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

# Calculate the number of files per merged file
num_files_per_merged = ceil(len(file_paths) / 16)

# Iterate over the desired merged files
for i in range(16):
    # Open the merged file for writing
    merged_file_path = f'/tmp/merged_{i}'
    with open(merged_file_path, 'w') as merged_file:
        # Determine the start and end indices for the current batch of files
        start_idx = i * num_files_per_merged
        end_idx = min((i + 1) * num_files_per_merged, len(file_paths))

        # Process each file in the current batch
        for file_path in file_paths[start_idx:end_idx]:
            try:
                # Open the file for reading
                with open(file_path, 'r') as f:
                    # Write the contents to the merged file
                    merged_file.write(f.read())
            except Exception as e:
                print(f'Error reading file {file_path}: {e}')

print('Merging complete.')

Run Code Interpreter

Running the following program: 

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import os
from math import ceil

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

# Calculate the number of files per merged file
num_files_per_merged = ceil(len(file_paths) / 16)

# Iterate over the desired merged files
for i in range(16):
    # Open the merged file for writing
    merged_file_path = f'/tmp/merged_{i}'
    with open(merged_file_path, 'w') as merged_file:
        # Determine the start and end indices for the current batch of files
        start_idx = i * num_files_per_merged
        end_idx = min((i + 1) * num_files_per_merged, len(file_paths))

        # Process each file in the current batch
        for file_path in file_paths[start_idx:end_idx]:
            try:
                # Open the file for reading
                with open(file_path, 'r') as f:
                    # Write the contents to the merged file
                    merged_file.write(f.read())
            except Exception as e:
                print(f'Error reading file {file_path}: {e}')

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

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 the trick: 

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import os
import math

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

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merged = math.ceil(num_files / 16)

# Create the merged files
for i in range(16):
    start = i * files_per_merged
    end = start + files_per_merged
    merged_file = f"/tmp/merged_{i}"

    with open(merged_file, "wb") as mf:
        for path in file_paths[start:end]:
            if os.path.exists(path):
                with open(path, "rb") as f:
                    mf.write(f.read())

print("Files merged successfully.")
Here's how the script works: 1. First, it reads the list of file paths from `/tmp/files` and stores them in the `file_paths` list. 2. Then, it calculates the number of files that should be included in each merged file by dividing the total number of files by 16 and rounding up using `math.ceil()`. 3. Next, it creates 16 merged files named `/tmp/merged_0`, `/tmp/merged_1`, ..., `/tmp/merged_15`. 4. For each merged file, it opens it in binary write mode (`"wb"`). 5. It then iterates over the corresponding subset of file paths from the `file_paths` list, using the `start` and `end` indices. 6. For each file path, it checks if the file exists using `os.path.exists()`. If the file exists, it opens it in binary read mode (`"rb"`), reads its content using `f.read()`, and writes it to the merged file using `mf.write()`. 7. After processing all files for a particular merged file, it moves on to the next merged file. 8. Finally, it prints a success message. Note that this script assumes that the files being merged are binary files. If you're merging text files, you may need to open them in text mode (`"r"` and `"w"`) instead of binary mode.

Extract Code

I extracted the following code from that output: 

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import os
import math

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

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merged = math.ceil(num_files / 16)

# Create the merged files
for i in range(16):
    start = i * files_per_merged
    end = start + files_per_merged
    merged_file = f"/tmp/merged_{i}"

    with open(merged_file, "wb") as mf:
        for path in file_paths[start:end]:
            if os.path.exists(path):
                with open(path, "rb") as f:
                    mf.write(f.read())

print("Files merged successfully.")

Run Code Interpreter

Running the following program: 

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import os
import math

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

# Calculate the number of files per merged file
num_files = len(file_paths)
files_per_merged = math.ceil(num_files / 16)

# Create the merged files
for i in range(16):
    start = i * files_per_merged
    end = start + files_per_merged
    merged_file = f"/tmp/merged_{i}"

    with open(merged_file, "wb") as mf:
        for path in file_paths[start:end]:
            if os.path.exists(path):
                with open(path, "rb") as f:
                    mf.write(f.read())

print("Files merged successfully.")
And got the output: Files merged successfully.

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 reads the file paths from `/tmp/files`, splits them into approximately equal-sized chunks, and then merges the content of each chunk into a separate file `/tmp/merged_%d`: 

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import os
from math import ceil

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

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

# Calculate the number of files needed
num_files = 16
chunk_size = ceil(len(file_paths) / num_files)

# Split the file paths into approximately equal-sized chunks
chunks = [file_paths[i:i+chunk_size] for i in range(0, len(file_paths), chunk_size)]

# Merge the content of each chunk into a separate file
for i, chunk in enumerate(chunks):
    with open(f'/tmp/merged_{i}', 'w') as merged_file:
        for file_path in chunk:
            try:
                with open(file_path, 'r') as f:
                    content = f.read()
                merged_file.write(content)
            except FileNotFoundError:
                print(f"Error: File {file_path} not found.")
            except Exception as e:
                print(f"Error: {e}")
Here's how the script works: 1. The file paths are read from `/tmp/files` and stored in a list called `file_paths`. 2. The newline characters are removed from the file paths using a list comprehension. 3. The number of files needed for merging is set to 16. 4. The file paths are split into approximately equal-sized chunks using a list comprehension and the `math.ceil` function to ensure that the last chunk is not empty. 5. For each chunk, a new file `/tmp/merged_%d` is created, where `%d` is the index of the chunk. 6. The content of each file in the chunk is read and appended to the corresponding merged file. 7. If a file is not found or an error occurs while reading a file, an error message is printed to the console. Note that this script assumes that all files can be read and merged successfully. If you encounter any issues, such as permission errors or large file sizes, you may need to modify the script accordingly.

Extract Code

I extracted the following code from that output: 

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import os
from math import ceil

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

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

# Calculate the number of files needed
num_files = 16
chunk_size = ceil(len(file_paths) / num_files)

# Split the file paths into approximately equal-sized chunks
chunks = [file_paths[i:i+chunk_size] for i in range(0, len(file_paths), chunk_size)]

# Merge the content of each chunk into a separate file
for i, chunk in enumerate(chunks):
    with open(f'/tmp/merged_{i}', 'w') as merged_file:
        for file_path in chunk:
            try:
                with open(file_path, 'r') as f:
                    content = f.read()
                merged_file.write(content)
            except FileNotFoundError:
                print(f"Error: File {file_path} not found.")
            except Exception as e:
                print(f"Error: {e}")

Run Code Interpreter

Running the following program: 

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import os
from math import ceil

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

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

# Calculate the number of files needed
num_files = 16
chunk_size = ceil(len(file_paths) / num_files)

# Split the file paths into approximately equal-sized chunks
chunks = [file_paths[i:i+chunk_size] for i in range(0, len(file_paths), chunk_size)]

# Merge the content of each chunk into a separate file
for i, chunk in enumerate(chunks):
    with open(f'/tmp/merged_{i}', 'w') as merged_file:
        for file_path in chunk:
            try:
                with open(file_path, 'r') as f:
                    content = f.read()
                merged_file.write(content)
            except FileNotFoundError:
                print(f"Error: File {file_path} not found.")
            except Exception as e:
                print(f"Error: {e}")
And got the output:

PyFunc

final: False Resulting in output: False