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

To solve this problem, we need to merge the contents of several thousand files into 16 output files such that each output file is approximately the same size. The solution involves distributing the files based on their sizes to ensure balanced output files. ### Approach 1. **Read File Paths**: Read the list of file paths from the input file `/tmp/files`. 2. **Collect File Sizes**: For each file path, determine the file size and handle any missing files by skipping them. 3. **Sort Files by Size**: Sort the files in descending order of their sizes. This helps in placing the largest files first, which aids in achieving a balanced distribution. 4. **Distribute Files into Buckets**: Use 16 buckets to distribute the files. For each file, place it in the bucket that currently has the smallest total size. This greedy approach helps in balancing the sizes across all buckets. 5. **Merge Files into Outputs**: For each bucket, write the contents of all its files into the corresponding output file, handling large files by reading and writing in chunks to avoid memory issues. ### Solution Code 

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import os

def main():
    # Read input file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Collect file sizes and paths, skipping those that don't exist
    files = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files.append((size, path))
        except FileNotFoundError:
            print(f"Warning: File {path} not found, skipping.")
            continue
    
    # Sort files by size in descending order to optimize distribution
    files.sort(reverse=True, key=lambda x: x[0])
    
    # Initialize 16 buckets to track total size and associated files
    buckets = [{'total': 0, 'files': []} for _ in range(16)]
    
    # Distribute each file into the bucket with the current smallest total size
    for size, path in files:
        min_bucket = min(buckets, key=lambda b: b['total'])
        min_bucket['files'].append(path)
        min_bucket['total'] += size
    
    # Write each bucket's files into the corresponding merged output file
    for i, bucket in enumerate(buckets):
        output_path = f'/tmp/merged_{i}'
        with open(output_path, 'wb') as outfile:
            for file_path in bucket['files']:
                try:
                    with open(file_path, 'rb') as infile:
                        while True:
                            chunk = infile.read(4096)  # Read in chunks to handle large files
                            if not chunk:
                                break
                            outfile.write(chunk)
                except FileNotFoundError:
                    print(f"Warning: File {file_path} not found during merging, skipping.")
                    continue

if __name__ == "__main__":
    main()
### Explanation 1. **Reading File Paths**: The input file `/tmp/files` is read line by line, and each line is stripped of any leading or trailing whitespace to get the file paths. 2. **Collecting File Sizes**: Each file's size is determined using `os.path.getsize`, and any files that are not found are skipped with a warning. 3. **Sorting Files**: Files are sorted in descending order of size to facilitate optimal distribution into buckets. 4. **Distributing into Buckets**: Each file is placed into the bucket that currently has the smallest total size. This ensures a balanced distribution of file sizes across all buckets. 5. **Writing Output Files**: Each bucket's files are written into the corresponding output file in chunks to efficiently handle large files and avoid high memory usage. This approach ensures that the merged files are approximately the same size by leveraging a greedy algorithm for distribution and efficient file handling techniques.

Extract Code

I extracted the following code from that output: 

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import os

def main():
    # Read input file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Collect file sizes and paths, skipping those that don't exist
    files = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files.append((size, path))
        except FileNotFoundError:
            print(f"Warning: File {path} not found, skipping.")
            continue
    
    # Sort files by size in descending order to optimize distribution
    files.sort(reverse=True, key=lambda x: x[0])
    
    # Initialize 16 buckets to track total size and associated files
    buckets = [{'total': 0, 'files': []} for _ in range(16)]
    
    # Distribute each file into the bucket with the current smallest total size
    for size, path in files:
        min_bucket = min(buckets, key=lambda b: b['total'])
        min_bucket['files'].append(path)
        min_bucket['total'] += size
    
    # Write each bucket's files into the corresponding merged output file
    for i, bucket in enumerate(buckets):
        output_path = f'/tmp/merged_{i}'
        with open(output_path, 'wb') as outfile:
            for file_path in bucket['files']:
                try:
                    with open(file_path, 'rb') as infile:
                        while True:
                            chunk = infile.read(4096)  # Read in chunks to handle large files
                            if not chunk:
                                break
                            outfile.write(chunk)
                except FileNotFoundError:
                    print(f"Warning: File {file_path} not found during merging, skipping.")
                    continue

if __name__ == "__main__":
    main()

Run Code Interpreter

Running the following program: 

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import os

def main():
    # Read input file paths
    with open('/tmp/files', 'r') as f:
        file_paths = [line.strip() for line in f if line.strip()]
    
    # Collect file sizes and paths, skipping those that don't exist
    files = []
    for path in file_paths:
        try:
            size = os.path.getsize(path)
            files.append((size, path))
        except FileNotFoundError:
            print(f"Warning: File {path} not found, skipping.")
            continue
    
    # Sort files by size in descending order to optimize distribution
    files.sort(reverse=True, key=lambda x: x[0])
    
    # Initialize 16 buckets to track total size and associated files
    buckets = [{'total': 0, 'files': []} for _ in range(16)]
    
    # Distribute each file into the bucket with the current smallest total size
    for size, path in files:
        min_bucket = min(buckets, key=lambda b: b['total'])
        min_bucket['files'].append(path)
        min_bucket['total'] += size
    
    # Write each bucket's files into the corresponding merged output file
    for i, bucket in enumerate(buckets):
        output_path = f'/tmp/merged_{i}'
        with open(output_path, 'wb') as outfile:
            for file_path in bucket['files']:
                try:
                    with open(file_path, 'rb') as infile:
                        while True:
                            chunk = infile.read(4096)  # Read in chunks to handle large files
                            if not chunk:
                                break
                            outfile.write(chunk)
                except FileNotFoundError:
                    print(f"Warning: File {file_path} not found during merging, skipping.")
                    continue

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

PyFunc

final: True Resulting in output: True