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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
import os

def merge_and_split_files(file_list_path, output_dir, num_splits=16):
    """Merges files listed in a file, splitting the content into multiple output files.

    Args:
        file_list_path (str): Path to the file containing the list of file paths to merge.
        output_dir (str): Directory to store the merged output files.
        num_splits (int, optional): Number of output files to create. Defaults to 16.
    """

    with open(file_list_path, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Calculate target size for each output file
    total_size = sum(os.path.getsize(file_path) for file_path in file_paths)
    target_size = total_size // num_splits

    # Create output file handles and track their sizes
    output_files = [open(os.path.join(output_dir, f"merged_{i}"), 'wb') for i in range(num_splits)]
    output_sizes = [0] * num_splits

    # Distribute file contents across output files
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the smallest output file
            min_size_index = output_sizes.index(min(output_sizes))
            outfile = output_files[min_size_index]

            # Copy data to the chosen output file
            while True:
                chunk = infile.read(4096)  # Read in chunks
                if not chunk:
                    break
                outfile.write(chunk)
                output_sizes[min_size_index] += len(chunk)

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

# Example usage:
file_list_path = "/tmp/files"
output_dir = "/tmp"  
num_splits = 16

merge_and_split_files(file_list_path, output_dir, num_splits)
**Explanation:** 1. **File Handling:** - Reads the file paths from the input file `/tmp/files`. - Opens output files named `merged_0`, `merged_1`, ..., `merged_15` in `/tmp` for writing in binary mode (`'wb'`). 2. **Sorting Files:** - Sorts the list of file paths in descending order of their file sizes using `os.path.getsize` as the key for sorting. 3. **Calculating Target Size:** - Calculates the total size of all files to be merged. - Determines the target size for each output file by dividing the total size by the number of splits. 4. **Distributing Data:** - Iterates through each file path: - Opens the current file for reading in binary mode (`'rb'`). - Finds the index of the output file with the smallest size so far. - Reads the current file in chunks (4KB in this case) to handle potentially large files efficiently. - Writes each chunk to the chosen output file and updates its size in the `output_sizes` list. 5. **Closing Files:** - Closes all output file handles to release resources. **How it ensures approximately equal sizes:** - **Largest File First:** By sorting the files by size (largest first), the script prioritizes filling up the output files with larger chunks of data initially. - **Smallest File Selection:** In each iteration, it selects the output file with the smallest current size. This ensures that data is distributed as evenly as possible, aiming to keep all output files at roughly the same size. **To run this script:** 1. Save it as a Python file (e.g., `merge_split.py`). 2. Make sure you have the file `/tmp/files` with the list of file paths. 3. Execute the script from your terminal using: `python merge_split.py`

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
import os

def merge_and_split_files(file_list_path, output_dir, num_splits=16):
    """Merges files listed in a file, splitting the content into multiple output files.

    Args:
        file_list_path (str): Path to the file containing the list of file paths to merge.
        output_dir (str): Directory to store the merged output files.
        num_splits (int, optional): Number of output files to create. Defaults to 16.
    """

    with open(file_list_path, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Calculate target size for each output file
    total_size = sum(os.path.getsize(file_path) for file_path in file_paths)
    target_size = total_size // num_splits

    # Create output file handles and track their sizes
    output_files = [open(os.path.join(output_dir, f"merged_{i}"), 'wb') for i in range(num_splits)]
    output_sizes = [0] * num_splits

    # Distribute file contents across output files
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the smallest output file
            min_size_index = output_sizes.index(min(output_sizes))
            outfile = output_files[min_size_index]

            # Copy data to the chosen output file
            while True:
                chunk = infile.read(4096)  # Read in chunks
                if not chunk:
                    break
                outfile.write(chunk)
                output_sizes[min_size_index] += len(chunk)

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

# Example usage:
file_list_path = "/tmp/files"
output_dir = "/tmp"  
num_splits = 16

merge_and_split_files(file_list_path, output_dir, num_splits)

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
import os

def merge_and_split_files(file_list_path, output_dir, num_splits=16):
    """Merges files listed in a file, splitting the content into multiple output files.

    Args:
        file_list_path (str): Path to the file containing the list of file paths to merge.
        output_dir (str): Directory to store the merged output files.
        num_splits (int, optional): Number of output files to create. Defaults to 16.
    """

    with open(file_list_path, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Calculate target size for each output file
    total_size = sum(os.path.getsize(file_path) for file_path in file_paths)
    target_size = total_size // num_splits

    # Create output file handles and track their sizes
    output_files = [open(os.path.join(output_dir, f"merged_{i}"), 'wb') for i in range(num_splits)]
    output_sizes = [0] * num_splits

    # Distribute file contents across output files
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the smallest output file
            min_size_index = output_sizes.index(min(output_sizes))
            outfile = output_files[min_size_index]

            # Copy data to the chosen output file
            while True:
                chunk = infile.read(4096)  # Read in chunks
                if not chunk:
                    break
                outfile.write(chunk)
                output_sizes[min_size_index] += len(chunk)

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

# Example usage:
file_list_path = "/tmp/files"
output_dir = "/tmp"  
num_splits = 16

merge_and_split_files(file_list_path, output_dir, num_splits)
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
import os

def merge_and_split_files(input_file_list, output_prefix, num_output_files):
    """Merges files listed in input_file_list and splits into multiple output files.

    Args:
        input_file_list (str): Path to a file containing a list of file paths, one per line.
        output_prefix (str): Prefix for output file names (e.g., '/tmp/merged_').
        num_output_files (int): Number of output files to create.
    """

    with open(input_file_list, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    output_file_handles = []
    output_file_sizes = [0] * num_output_files

    # Open output files in write binary mode
    for i in range(num_output_files):
        file_path = f"{output_prefix}{i}"
        output_file_handles.append(open(file_path, 'wb'))

    # Distribute file content
    for file_path in file_paths:
        smallest_file_index = output_file_sizes.index(min(output_file_sizes))
        with open(file_path, 'rb') as infile:
            for chunk in iter(lambda: infile.read(4096), b''):  # Read in chunks
                output_file_handles[smallest_file_index].write(chunk)
                output_file_sizes[smallest_file_index] += len(chunk)

    # Close all output files
    for file_handle in output_file_handles:
        file_handle.close()

if __name__ == "__main__":
    input_file_list = "/tmp/files"
    output_prefix = "/tmp/merged_"
    num_output_files = 16
    merge_and_split_files(input_file_list, output_prefix, num_output_files)
**Explanation:** 1. **File Handling and Sorting:** - Reads file paths from `input_file_list`. - Sorts the file paths in descending order based on their file sizes using `os.path.getsize` and `reverse=True`. 2. **Output File Management:** - Creates a list `output_file_handles` to store file handles for each output file. - Creates a list `output_file_sizes` to keep track of the current size of each output file, initialized with zeros. - Opens all output files in write binary (`'wb'`) mode. 3. **Content Distribution:** - Iterates through each file path in the sorted `file_paths` list. - Finds the index of the output file with the smallest size using `output_file_sizes.index(min(output_file_sizes))`. - Reads the input file in chunks of 4096 bytes using a loop with `iter(lambda: infile.read(4096), b'')`. - Writes each chunk to the selected smallest output file. - Updates the corresponding output file size in `output_file_sizes`. 4. **Closing Files:** - Closes all output file handles using a loop to ensure data is written and resources are released. **How to run the script:** 1. Save the code as a Python file (e.g., `merge_split.py`). 2. Make sure you have a file named `/tmp/files` containing the list of file paths you want to merge, one path per line. 3. Run the script from your terminal: `python merge_split.py` This will create 16 files (`/tmp/merged_0` to `/tmp/merged_15`) with approximately equal sizes, containing the merged content from the input files.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
import os

def merge_and_split_files(input_file_list, output_prefix, num_output_files):
    """Merges files listed in input_file_list and splits into multiple output files.

    Args:
        input_file_list (str): Path to a file containing a list of file paths, one per line.
        output_prefix (str): Prefix for output file names (e.g., '/tmp/merged_').
        num_output_files (int): Number of output files to create.
    """

    with open(input_file_list, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    output_file_handles = []
    output_file_sizes = [0] * num_output_files

    # Open output files in write binary mode
    for i in range(num_output_files):
        file_path = f"{output_prefix}{i}"
        output_file_handles.append(open(file_path, 'wb'))

    # Distribute file content
    for file_path in file_paths:
        smallest_file_index = output_file_sizes.index(min(output_file_sizes))
        with open(file_path, 'rb') as infile:
            for chunk in iter(lambda: infile.read(4096), b''):  # Read in chunks
                output_file_handles[smallest_file_index].write(chunk)
                output_file_sizes[smallest_file_index] += len(chunk)

    # Close all output files
    for file_handle in output_file_handles:
        file_handle.close()

if __name__ == "__main__":
    input_file_list = "/tmp/files"
    output_prefix = "/tmp/merged_"
    num_output_files = 16
    merge_and_split_files(input_file_list, output_prefix, num_output_files)

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
import os

def merge_and_split_files(input_file_list, output_prefix, num_output_files):
    """Merges files listed in input_file_list and splits into multiple output files.

    Args:
        input_file_list (str): Path to a file containing a list of file paths, one per line.
        output_prefix (str): Prefix for output file names (e.g., '/tmp/merged_').
        num_output_files (int): Number of output files to create.
    """

    with open(input_file_list, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    output_file_handles = []
    output_file_sizes = [0] * num_output_files

    # Open output files in write binary mode
    for i in range(num_output_files):
        file_path = f"{output_prefix}{i}"
        output_file_handles.append(open(file_path, 'wb'))

    # Distribute file content
    for file_path in file_paths:
        smallest_file_index = output_file_sizes.index(min(output_file_sizes))
        with open(file_path, 'rb') as infile:
            for chunk in iter(lambda: infile.read(4096), b''):  # Read in chunks
                output_file_handles[smallest_file_index].write(chunk)
                output_file_sizes[smallest_file_index] += len(chunk)

    # Close all output files
    for file_handle in output_file_handles:
        file_handle.close()

if __name__ == "__main__":
    input_file_list = "/tmp/files"
    output_prefix = "/tmp/merged_"
    num_output_files = 16
    merge_and_split_files(input_file_list, output_prefix, num_output_files)
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
import os

def merge_and_split_files(input_file_list, output_prefix, num_output_files):
    """Merges files listed in input_file_list and splits the content into
    num_output_files with approximately equal size.

    Args:
        input_file_list (str): Path to the file containing a list of file paths.
        output_prefix (str): Prefix for the output file names.
        num_output_files (int): Number of output files to create.
    """

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

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Calculate target size for each output file
    total_size = sum(os.path.getsize(file_path) for file_path in file_paths)
    target_size = total_size // num_output_files

    # Initialize output files and their sizes
    output_files = [open(f"{output_prefix}_{i}", "wb") for i in range(num_output_files)]
    output_sizes = [0] * num_output_files

    # Distribute file content across output files
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the smallest output file
            min_size_index = output_sizes.index(min(output_sizes))
            outfile = output_files[min_size_index]

            # Copy content to the smallest output file
            while True:
                chunk = infile.read(4096)  # Read in chunks
                if not chunk:
                    break
                outfile.write(chunk)
                output_sizes[min_size_index] += len(chunk)

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

# Example usage:
input_file_list = "/tmp/files"
output_prefix = "/tmp/merged"
num_output_files = 16

merge_and_split_files(input_file_list, output_prefix, num_output_files)
**Explanation:** 1. **File Handling and Sorting:** - The script reads the list of file paths from the specified input file (`/tmp/files`). - It sorts the file paths based on their sizes in descending order using `os.path.getsize` and `reverse=True`. 2. **Calculating Target Size:** - It calculates the total size of all files and divides it by the desired number of output files (`16`) to determine the target size for each output file. 3. **Distributing File Content:** - The script iterates through the sorted file paths. - For each file: - It opens the file in binary read mode (`'rb'`). - It finds the output file with the smallest current size. - It reads the input file in chunks (4KB in this case) and writes the chunks to the chosen output file until the entire input file is processed. - It updates the size of the output file accordingly. 4. **Output File Management:** - The script opens the output files (`/tmp/merged_0` to `/tmp/merged_15`) in binary write mode (`'wb'`) at the beginning. - It closes all output files after processing all input files. **How it ensures approximately equal size:** - By sorting the files by size (largest first) and always adding content to the smallest output file, the script aims to distribute the data evenly. - This approach doesn't guarantee perfectly equal sizes, but it provides a reasonable approximation, especially when dealing with a large number of files.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
import os

def merge_and_split_files(input_file_list, output_prefix, num_output_files):
    """Merges files listed in input_file_list and splits the content into
    num_output_files with approximately equal size.

    Args:
        input_file_list (str): Path to the file containing a list of file paths.
        output_prefix (str): Prefix for the output file names.
        num_output_files (int): Number of output files to create.
    """

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

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Calculate target size for each output file
    total_size = sum(os.path.getsize(file_path) for file_path in file_paths)
    target_size = total_size // num_output_files

    # Initialize output files and their sizes
    output_files = [open(f"{output_prefix}_{i}", "wb") for i in range(num_output_files)]
    output_sizes = [0] * num_output_files

    # Distribute file content across output files
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the smallest output file
            min_size_index = output_sizes.index(min(output_sizes))
            outfile = output_files[min_size_index]

            # Copy content to the smallest output file
            while True:
                chunk = infile.read(4096)  # Read in chunks
                if not chunk:
                    break
                outfile.write(chunk)
                output_sizes[min_size_index] += len(chunk)

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

# Example usage:
input_file_list = "/tmp/files"
output_prefix = "/tmp/merged"
num_output_files = 16

merge_and_split_files(input_file_list, output_prefix, num_output_files)

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
import os

def merge_and_split_files(input_file_list, output_prefix, num_output_files):
    """Merges files listed in input_file_list and splits the content into
    num_output_files with approximately equal size.

    Args:
        input_file_list (str): Path to the file containing a list of file paths.
        output_prefix (str): Prefix for the output file names.
        num_output_files (int): Number of output files to create.
    """

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

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Calculate target size for each output file
    total_size = sum(os.path.getsize(file_path) for file_path in file_paths)
    target_size = total_size // num_output_files

    # Initialize output files and their sizes
    output_files = [open(f"{output_prefix}_{i}", "wb") for i in range(num_output_files)]
    output_sizes = [0] * num_output_files

    # Distribute file content across output files
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the smallest output file
            min_size_index = output_sizes.index(min(output_sizes))
            outfile = output_files[min_size_index]

            # Copy content to the smallest output file
            while True:
                chunk = infile.read(4096)  # Read in chunks
                if not chunk:
                    break
                outfile.write(chunk)
                output_sizes[min_size_index] += len(chunk)

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

# Example usage:
input_file_list = "/tmp/files"
output_prefix = "/tmp/merged"
num_output_files = 16

merge_and_split_files(input_file_list, output_prefix, num_output_files)
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
import os

def merge_and_split_files(input_file_list, output_prefix, num_splits):
    """Merges files listed in input_file_list and splits content into
    num_splits output files.

    Args:
        input_file_list (str): Path to a file containing a list of file paths,
                               one per line.
        output_prefix (str): Prefix for output file names.
        num_splits (int): Number of output files to create.
    """

    with open(input_file_list, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Initialize output file handles and sizes
    output_files = [open(f"{output_prefix}_{i}", 'wb') for i in range(num_splits)]
    output_file_sizes = [0] * num_splits

    # Iterate through input files and write to the smallest output file
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the index of the smallest output file
            min_size_index = output_file_sizes.index(min(output_file_sizes))
            outfile = output_files[min_size_index]

            # Write the contents of the input file to the output file
            for chunk in iter(lambda: infile.read(4096), b''):  # Read in chunks
                outfile.write(chunk)
                output_file_sizes[min_size_index] += len(chunk)

    # Close all output file handles
    for outfile in output_files:
        outfile.close()

if __name__ == "__main__":
    input_file_list = "/tmp/files"
    output_prefix = "/tmp/merged"
    num_splits = 16

    merge_and_split_files(input_file_list, output_prefix, num_splits)
**Explanation:** 1. **File Reading and Sorting:** - Reads the file paths from the input file (`/tmp/files`). - Sorts the file paths based on their size in descending order using `os.path.getsize` and `reverse=True`. 2. **Output File Initialization:** - Creates `num_splits` (16 in this case) output files with names like `/tmp/merged_0`, `/tmp/merged_1`, etc., in binary write mode (`'wb'`). - Initializes a list `output_file_sizes` to keep track of the current size of each output file. 3. **Merging and Splitting Logic:** - Iterates through each sorted input file path. - For each input file: - Finds the index of the output file with the smallest size using `output_file_sizes.index(min(output_file_sizes))`. - Opens the corresponding output file for writing. - Reads the input file in chunks (using a chunk size of 4096 bytes) to handle potentially large files efficiently. - Writes each chunk to the chosen output file. - Updates the size of the output file in `output_file_sizes`. 4. **Closing Files:** - Closes all the opened output file handles to release resources. **How the Code Ensures Even Distribution:** - **Sorting by Size (Largest First):** By processing the largest files first, the code tries to distribute the bulk of the data more evenly across the output files initially. - **Writing to the Smallest File:** In each iteration, the code always writes to the output file that currently has the smallest size. This dynamic allocation strategy helps in maintaining a relatively balanced distribution of data among the output files. **Running the Script:** 1. Save the code as a Python file (e.g., `merge_split.py`). 2. Make sure you have the input file (`/tmp/files`) containing the list of file paths. 3. Run the script from your terminal: `python merge_split.py` This will create the 16 merged files (`/tmp/merged_0` to `/tmp/merged_15`) with approximately equal sizes.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
import os

def merge_and_split_files(input_file_list, output_prefix, num_splits):
    """Merges files listed in input_file_list and splits content into
    num_splits output files.

    Args:
        input_file_list (str): Path to a file containing a list of file paths,
                               one per line.
        output_prefix (str): Prefix for output file names.
        num_splits (int): Number of output files to create.
    """

    with open(input_file_list, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Initialize output file handles and sizes
    output_files = [open(f"{output_prefix}_{i}", 'wb') for i in range(num_splits)]
    output_file_sizes = [0] * num_splits

    # Iterate through input files and write to the smallest output file
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the index of the smallest output file
            min_size_index = output_file_sizes.index(min(output_file_sizes))
            outfile = output_files[min_size_index]

            # Write the contents of the input file to the output file
            for chunk in iter(lambda: infile.read(4096), b''):  # Read in chunks
                outfile.write(chunk)
                output_file_sizes[min_size_index] += len(chunk)

    # Close all output file handles
    for outfile in output_files:
        outfile.close()

if __name__ == "__main__":
    input_file_list = "/tmp/files"
    output_prefix = "/tmp/merged"
    num_splits = 16

    merge_and_split_files(input_file_list, output_prefix, num_splits)

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
import os

def merge_and_split_files(input_file_list, output_prefix, num_splits):
    """Merges files listed in input_file_list and splits content into
    num_splits output files.

    Args:
        input_file_list (str): Path to a file containing a list of file paths,
                               one per line.
        output_prefix (str): Prefix for output file names.
        num_splits (int): Number of output files to create.
    """

    with open(input_file_list, 'r') as f:
        file_paths = [line.strip() for line in f]

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Initialize output file handles and sizes
    output_files = [open(f"{output_prefix}_{i}", 'wb') for i in range(num_splits)]
    output_file_sizes = [0] * num_splits

    # Iterate through input files and write to the smallest output file
    for file_path in file_paths:
        with open(file_path, 'rb') as infile:
            # Find the index of the smallest output file
            min_size_index = output_file_sizes.index(min(output_file_sizes))
            outfile = output_files[min_size_index]

            # Write the contents of the input file to the output file
            for chunk in iter(lambda: infile.read(4096), b''):  # Read in chunks
                outfile.write(chunk)
                output_file_sizes[min_size_index] += len(chunk)

    # Close all output file handles
    for outfile in output_files:
        outfile.close()

if __name__ == "__main__":
    input_file_list = "/tmp/files"
    output_prefix = "/tmp/merged"
    num_splits = 16

    merge_and_split_files(input_file_list, output_prefix, num_splits)
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
import os

def merge_and_split(input_file, output_prefix, num_splits=16):
    """Merges files listed in input_file and splits them into num_splits files.

    Args:
        input_file: Path to the file containing a list of file paths, one per line.
        output_prefix: Prefix for the output files (e.g., '/tmp/merged_').
        num_splits: Number of output files to create.
    """

    with open(input_file, 'r') as f:
        file_paths = f.read().splitlines()

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Initialize output file handles and sizes
    output_files = [open(f"{output_prefix}{i}", "wb") for i in range(num_splits)]
    output_sizes = [0] * num_splits

    # Iterate through the sorted file list
    for file_path in file_paths:
        # Find the smallest output file
        min_size_index = output_sizes.index(min(output_sizes))

        # Open the input file for reading in binary mode
        with open(file_path, 'rb') as infile:
            # Read and write data in chunks to avoid memory issues with large files
            chunk_size = 4096  # Adjust chunk size as needed
            chunk = infile.read(chunk_size)
            while chunk:
                output_files[min_size_index].write(chunk)
                output_sizes[min_size_index] += len(chunk)
                chunk = infile.read(chunk_size)

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

# Example usage:
input_file = '/tmp/files'
output_prefix = '/tmp/merged_'
num_splits = 16

merge_and_split(input_file, output_prefix, num_splits)
**Explanation:** 1. **Read file paths:** The code reads the list of file paths from the input file. 2. **Sort by size:** It sorts the file paths based on their file sizes in descending order, ensuring the largest file is processed first. 3. **Initialize output files:** It creates the specified number of output files with the given prefix and opens them in binary write mode. 4. **Distribute file content:** - It iterates through the sorted file list. - For each file, it identifies the output file with the smallest current size. - It reads the input file in chunks and writes the content to the chosen output file, updating the output file's size accordingly. - This process ensures that the files are distributed as evenly as possible among the output files. 5. **Close files:** Finally, it closes all the opened output file handles. This script effectively merges the content of the input files while aiming for a relatively balanced distribution of data across the specified number of output files.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
import os

def merge_and_split(input_file, output_prefix, num_splits=16):
    """Merges files listed in input_file and splits them into num_splits files.

    Args:
        input_file: Path to the file containing a list of file paths, one per line.
        output_prefix: Prefix for the output files (e.g., '/tmp/merged_').
        num_splits: Number of output files to create.
    """

    with open(input_file, 'r') as f:
        file_paths = f.read().splitlines()

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Initialize output file handles and sizes
    output_files = [open(f"{output_prefix}{i}", "wb") for i in range(num_splits)]
    output_sizes = [0] * num_splits

    # Iterate through the sorted file list
    for file_path in file_paths:
        # Find the smallest output file
        min_size_index = output_sizes.index(min(output_sizes))

        # Open the input file for reading in binary mode
        with open(file_path, 'rb') as infile:
            # Read and write data in chunks to avoid memory issues with large files
            chunk_size = 4096  # Adjust chunk size as needed
            chunk = infile.read(chunk_size)
            while chunk:
                output_files[min_size_index].write(chunk)
                output_sizes[min_size_index] += len(chunk)
                chunk = infile.read(chunk_size)

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

# Example usage:
input_file = '/tmp/files'
output_prefix = '/tmp/merged_'
num_splits = 16

merge_and_split(input_file, output_prefix, num_splits)

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
import os

def merge_and_split(input_file, output_prefix, num_splits=16):
    """Merges files listed in input_file and splits them into num_splits files.

    Args:
        input_file: Path to the file containing a list of file paths, one per line.
        output_prefix: Prefix for the output files (e.g., '/tmp/merged_').
        num_splits: Number of output files to create.
    """

    with open(input_file, 'r') as f:
        file_paths = f.read().splitlines()

    # Sort files by size in descending order
    file_paths.sort(key=os.path.getsize, reverse=True)

    # Initialize output file handles and sizes
    output_files = [open(f"{output_prefix}{i}", "wb") for i in range(num_splits)]
    output_sizes = [0] * num_splits

    # Iterate through the sorted file list
    for file_path in file_paths:
        # Find the smallest output file
        min_size_index = output_sizes.index(min(output_sizes))

        # Open the input file for reading in binary mode
        with open(file_path, 'rb') as infile:
            # Read and write data in chunks to avoid memory issues with large files
            chunk_size = 4096  # Adjust chunk size as needed
            chunk = infile.read(chunk_size)
            while chunk:
                output_files[min_size_index].write(chunk)
                output_sizes[min_size_index] += len(chunk)
                chunk = infile.read(chunk_size)

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

# Example usage:
input_file = '/tmp/files'
output_prefix = '/tmp/merged_'
num_splits = 16

merge_and_split(input_file, output_prefix, num_splits)
And got the output:

PyFunc

final: True Resulting in output: True