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

To solve this problem, we need to merge the contents of several thousand files listed in `/tmp/files` into 16 output files such that each output file is approximately the same size. The strategy involves sorting the input files by size in descending order and distributing each file to the currently smallest output file to ensure balanced sizes. ### Approach 1. **Read File Paths**: Read the list of file paths from `/tmp/files`. 2. **Collect File Sizes**: Determine the size of each file to facilitate sorting. 3. **Sort Files by Size**: Sort the files in descending order of their sizes to handle the largest files first. 4. **Initialize Output Files**: Create 16 output files in `/tmp` named `merged_0` to `merged_15`. 5. **Distribute Files**: For each file, read its content and append it to the output file that currently has the smallest total size. This ensures that the output files remain balanced in size. ### Solution Code 

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import os

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

    # Collect file paths with their sizes
    files_with_size = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files_with_size.append((path, size))
        except OSError:
            # Skip files that can't be accessed
            continue

    # Sort files by size in descending order
    sorted_files = sorted(files_with_size, key=lambda x: -x[1])

    # Initialize output files and track their sizes
    output_sizes = [0] * 16
    output_handles = []
    for i in range(16):
        output_path = f'/tmp/merged_{i}'
        # Open in write-binary mode to overwrite existing files
        fh = open(output_path, 'wb')
        output_handles.append(fh)

    # Distribute each file to the smallest output
    for path, size in sorted_files:
        # Find the index of the smallest output
        min_size = min(output_sizes)
        chosen_index = output_sizes.index(min_size)

        # Read and write the file content
        try:
            with open(path, 'rb') as infile:
                content = infile.read()
            output_handles[chosen_index].write(content)
            output_sizes[chosen_index] += size
        except OSError:
            # Skip files that can't be read
            continue

    # Close all output handles
    for fh in output_handles:
        fh.close()

if __name__ == "__main__":
    main()
### Explanation 1. **Reading File Paths**: The script reads all file paths from `/tmp/files` and processes each line to get a list of file paths. 2. **File Size Collection**: For each file path, the script retrieves the file size using `os.path.getsize` and stores the path and size as a tuple. 3. **Sorting Files**: The files are sorted in descending order based on their sizes. This ensures that the largest files are processed first, which helps in balancing the sizes of the output files more effectively. 4. **Output Initialization**: The script initializes 16 output files in write-binary mode (`'wb'`), which truncates any existing content, ensuring a fresh start each time the script runs. 5. **File Distribution**: Each file is read and appended to the output file with the smallest current size. This is done by tracking the sizes of the output files and updating them as each file is distributed. The use of binary mode ensures that all file types are handled correctly. This approach efficiently balances the sizes of the output files by leveraging a greedy algorithm, always placing the next largest file into the currently smallest output file, resulting in approximately equal sizes across all 16 output files.

Extract Code

I extracted the following code from that output: 

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import os

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

    # Collect file paths with their sizes
    files_with_size = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files_with_size.append((path, size))
        except OSError:
            # Skip files that can't be accessed
            continue

    # Sort files by size in descending order
    sorted_files = sorted(files_with_size, key=lambda x: -x[1])

    # Initialize output files and track their sizes
    output_sizes = [0] * 16
    output_handles = []
    for i in range(16):
        output_path = f'/tmp/merged_{i}'
        # Open in write-binary mode to overwrite existing files
        fh = open(output_path, 'wb')
        output_handles.append(fh)

    # Distribute each file to the smallest output
    for path, size in sorted_files:
        # Find the index of the smallest output
        min_size = min(output_sizes)
        chosen_index = output_sizes.index(min_size)

        # Read and write the file content
        try:
            with open(path, 'rb') as infile:
                content = infile.read()
            output_handles[chosen_index].write(content)
            output_sizes[chosen_index] += size
        except OSError:
            # Skip files that can't be read
            continue

    # Close all output handles
    for fh in output_handles:
        fh.close()

if __name__ == "__main__":
    main()

Run Code Interpreter

Running the following program: 

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import os

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

    # Collect file paths with their sizes
    files_with_size = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files_with_size.append((path, size))
        except OSError:
            # Skip files that can't be accessed
            continue

    # Sort files by size in descending order
    sorted_files = sorted(files_with_size, key=lambda x: -x[1])

    # Initialize output files and track their sizes
    output_sizes = [0] * 16
    output_handles = []
    for i in range(16):
        output_path = f'/tmp/merged_{i}'
        # Open in write-binary mode to overwrite existing files
        fh = open(output_path, 'wb')
        output_handles.append(fh)

    # Distribute each file to the smallest output
    for path, size in sorted_files:
        # Find the index of the smallest output
        min_size = min(output_sizes)
        chosen_index = output_sizes.index(min_size)

        # Read and write the file content
        try:
            with open(path, 'rb') as infile:
                content = infile.read()
            output_handles[chosen_index].write(content)
            output_sizes[chosen_index] += size
        except OSError:
            # Skip files that can't be read
            continue

    # Close all output handles
    for fh in output_handles:
        fh.close()

if __name__ == "__main__":
    main()
And got the output:

PyFunc

final: True Resulting in output: True