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

Below is one complete solution. It first reads all file paths from /tmp/files, gets each file’s length, sorts them largest‐first, and then “bins” them into 16 output files by always appending the next file to the currently smallest total. Finally, it concatenates the contents of the assigned files into /tmp/merged_0 … /tmp/merged_15. You can run this script with Python 3. ------------------------------------------------------------ #!/usr/bin/env python3 """ Merge several thousand files (whose paths are listed one-per-line in /tmp/files) into 16 merged files (/tmp/merged_0 ... /tmp/merged_15) with roughly equal total file sizes. Files are processed in order of descending file size and each file is assigned to the merged output that currently has the smallest total size. """ import os import heapq import sys import shutil # Number of bins/output files NUM_BINS = 16 def get_file_size(file_path): try: return os.path.getsize(file_path) except OSError as e: sys.stderr.write(f"Error reading {file_path}: {e}\n") return 0 def main(): files_list_path = "/tmp/files" try: with open(files_list_path, 'r') as f: # Remove whitespace and skip empty lines. file_paths = [line.strip() for line in f if line.strip()] except Exception as e: sys.stderr.write(f"Error reading list file {files_list_path}: {e}\n") sys.exit(1) # Build list of (file_path, size) file_infos = [] for path in file_paths: size = get_file_size(path) file_infos.append((path, size)) # Sort files by size descending (largest first) file_infos.sort(key=lambda x: x[1], reverse=True) # Setup bins. Each bin will be stored as: # (total_bytes, bin_index, list_of_file_paths) # Use a heap (min-heap) to always extract the bin with smallest current total. bins = [] for i in range(NUM_BINS): heapq.heappush(bins, (0, i, [])) # Greedy assignment: for each file, pop the bin with smallest total size, # add the file to that bin, then push the updated bin back. for file_path, size in file_infos: total, bin_index, paths = heapq.heappop(bins) paths.append(file_path) total += size heapq.heappush(bins, (total, bin_index, paths)) # The bins heap now contains the assignments. # In order to write out the contents, we can iterate over the heap, # but note that the bins may be in arbitrary order. # So we gather them into a sorted list by bin_index. bin_assignments = [None] * NUM_BINS while bins: total, bin_index, paths = heapq.heappop(bins) bin_assignments[bin_index] = paths # Merge files from each bin: write them consecutively into the merged output file. # Here we perform a streamed copy (reading chunks) so even very large files are supported. CHUNK_SIZE = 1024 * 1024 # 1MB for i, paths in enumerate(bin_assignments): dest_file = f"/tmp/merged_{i}" with open(dest_file, 'wb') as outf: for src_path in paths: try: with open(src_path, 'rb') as inf: # Copy in chunks: shutil.copyfileobj(inf, outf, CHUNK_SIZE) except Exception as e: sys.stderr.write(f"Error processing {src_path}: {e}\n") print("Merging complete.") if __name__ == '__main__': main() ------------------------------------------------------------ How the script works: 1. It reads file paths from /tmp/files. 2. It gets the size for each file and sorts the list in descending order. 3. Using a heap for the 16 bins, each file is appended to the bin that currently has the smallest total size. 4. Finally, each output file (/tmp/merged_i) is created by concatenating the content of files assigned to that bin. 5. Errors opening files will be written to stderr. This solution should meet your requirements.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
"""
Merge several thousand files (whose paths are listed one-per-line in /tmp/files)
into 16 merged files (/tmp/merged_0 ... /tmp/merged_15) with roughly equal total
file sizes. Files are processed in order of descending file size and each file
is assigned to the merged output that currently has the smallest total size.
"""

import os
import heapq
import sys
import shutil

# Number of bins/output files
NUM_BINS = 16

def get_file_size(file_path):
    try:
        return os.path.getsize(file_path)
    except OSError as e:
        sys.stderr.write(f"Error reading {file_path}: {e}\n")
        return 0

def main():
    files_list_path = "/tmp/files"
    try:
        with open(files_list_path, 'r') as f:
            # Remove whitespace and skip empty lines.
            file_paths = [line.strip() for line in f if line.strip()]
    except Exception as e:
        sys.stderr.write(f"Error reading list file {files_list_path}: {e}\n")
        sys.exit(1)

    # Build list of (file_path, size)
    file_infos = []
    for path in file_paths:
        size = get_file_size(path)
        file_infos.append((path, size))

    # Sort files by size descending (largest first)
    file_infos.sort(key=lambda x: x[1], reverse=True)

    # Setup bins. Each bin will be stored as:
    # (total_bytes, bin_index, list_of_file_paths)
    # Use a heap (min-heap) to always extract the bin with smallest current total.
    bins = []
    for i in range(NUM_BINS):
        heapq.heappush(bins, (0, i, []))

    # Greedy assignment: for each file, pop the bin with smallest total size,
    # add the file to that bin, then push the updated bin back.
    for file_path, size in file_infos:
        total, bin_index, paths = heapq.heappop(bins)
        paths.append(file_path)
        total += size
        heapq.heappush(bins, (total, bin_index, paths))

    # The bins heap now contains the assignments.
    # In order to write out the contents, we can iterate over the heap,
    # but note that the bins may be in arbitrary order.
    # So we gather them into a sorted list by bin_index.
    bin_assignments = [None] * NUM_BINS
    while bins:
        total, bin_index, paths = heapq.heappop(bins)
        bin_assignments[bin_index] = paths

    # Merge files from each bin: write them consecutively into the merged output file.
    # Here we perform a streamed copy (reading chunks) so even very large files are supported.
    CHUNK_SIZE = 1024 * 1024  # 1MB

    for i, paths in enumerate(bin_assignments):
        dest_file = f"/tmp/merged_{i}"
        with open(dest_file, 'wb') as outf:
            for src_path in paths:
                try:
                    with open(src_path, 'rb') as inf:
                        # Copy in chunks:
                        shutil.copyfileobj(inf, outf, CHUNK_SIZE)
                except Exception as e:
                    sys.stderr.write(f"Error processing {src_path}: {e}\n")
    print("Merging complete.")

if __name__ == '__main__':
    main()

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3
"""
Merge several thousand files (whose paths are listed one-per-line in /tmp/files)
into 16 merged files (/tmp/merged_0 ... /tmp/merged_15) with roughly equal total
file sizes. Files are processed in order of descending file size and each file
is assigned to the merged output that currently has the smallest total size.
"""

import os
import heapq
import sys
import shutil

# Number of bins/output files
NUM_BINS = 16

def get_file_size(file_path):
    try:
        return os.path.getsize(file_path)
    except OSError as e:
        sys.stderr.write(f"Error reading {file_path}: {e}\n")
        return 0

def main():
    files_list_path = "/tmp/files"
    try:
        with open(files_list_path, 'r') as f:
            # Remove whitespace and skip empty lines.
            file_paths = [line.strip() for line in f if line.strip()]
    except Exception as e:
        sys.stderr.write(f"Error reading list file {files_list_path}: {e}\n")
        sys.exit(1)

    # Build list of (file_path, size)
    file_infos = []
    for path in file_paths:
        size = get_file_size(path)
        file_infos.append((path, size))

    # Sort files by size descending (largest first)
    file_infos.sort(key=lambda x: x[1], reverse=True)

    # Setup bins. Each bin will be stored as:
    # (total_bytes, bin_index, list_of_file_paths)
    # Use a heap (min-heap) to always extract the bin with smallest current total.
    bins = []
    for i in range(NUM_BINS):
        heapq.heappush(bins, (0, i, []))

    # Greedy assignment: for each file, pop the bin with smallest total size,
    # add the file to that bin, then push the updated bin back.
    for file_path, size in file_infos:
        total, bin_index, paths = heapq.heappop(bins)
        paths.append(file_path)
        total += size
        heapq.heappush(bins, (total, bin_index, paths))

    # The bins heap now contains the assignments.
    # In order to write out the contents, we can iterate over the heap,
    # but note that the bins may be in arbitrary order.
    # So we gather them into a sorted list by bin_index.
    bin_assignments = [None] * NUM_BINS
    while bins:
        total, bin_index, paths = heapq.heappop(bins)
        bin_assignments[bin_index] = paths

    # Merge files from each bin: write them consecutively into the merged output file.
    # Here we perform a streamed copy (reading chunks) so even very large files are supported.
    CHUNK_SIZE = 1024 * 1024  # 1MB

    for i, paths in enumerate(bin_assignments):
        dest_file = f"/tmp/merged_{i}"
        with open(dest_file, 'wb') as outf:
            for src_path in paths:
                try:
                    with open(src_path, 'rb') as inf:
                        # Copy in chunks:
                        shutil.copyfileobj(inf, outf, CHUNK_SIZE)
                except Exception as e:
                    sys.stderr.write(f"Error processing {src_path}: {e}\n")
    print("Merging complete.")

if __name__ == '__main__':
    main()
And got the output: Merging complete.

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

Below is one complete solution. This script: • Reads the list of file paths from "/tmp/files" (one per line). • Determines each file’s size. • Sorts the files by size (largest first). • Opens 16 output files (/tmp/merged_0, …, /tmp/merged_15) and assigns each output file a running total size of bytes written. • Iterates over the sorted list and, for each input file, appends its contents to the output file that currently has the smallest size. • Closes all files when done. Save the following code as, for example, merge_split.py and run it with Python. -------------------------------------------------------- #!/usr/bin/env python3 import os def main(): # Read the list of input file paths (one file per line) input_list_path = '/tmp/files' try: with open(input_list_path, 'r') as f: input_paths = [line.strip() for line in f if line.strip()] except Exception as e: print(f"Error opening {input_list_path}: {e}") return # Build a list of tuples (filepath, size) for all input files. files_with_size = [] for path in input_paths: try: size = os.path.getsize(path) files_with_size.append((path, size)) except OSError as e: print(f"Error getting size for {path}: {e} - Skipping.") continue # Sort the list by file size in descending order (largest first) files_with_size.sort(key=lambda x: x[1], reverse=True) # Prepare 16 output files, and keep track of their current size. num_outputs = 16 out_fhs = {} out_sizes = {} for i in range(num_outputs): out_name = f"/tmp/merged_{i}" try: out_fhs[i] = open(out_name, 'wb') out_sizes[i] = 0 except Exception as e: print(f"Error opening {out_name} for writing: {e}") for fh in out_fhs.values(): fh.close() return # For each input file (largest first), append its content # to the output file that currently has the smallest size. for file_path, size in files_with_size: # Find output file index with smallest copied size so far. smallest_idx = min(out_sizes, key=out_sizes.get) try: with open(file_path, 'rb') as in_f: while True: chunk = in_f.read(8192) if not chunk: break out_fhs[smallest_idx].write(chunk) # Update the running total for that output. out_sizes[smallest_idx] += size except Exception as e: print(f"Error processing {file_path}: {e}") # Clean up: close all output files. for fh in out_fhs.values(): fh.close() if __name__ == '__main__': main() -------------------------------------------------------- Explanation: 1. The script first reads the file paths from "/tmp/files" and trims whitespace. 2. It collects each file’s size using os.path.getsize(). Files that can’t be accessed are skipped with an error message. 3. The file list is sorted largest-first so that the largest files are assigned first. 4. Sixteen output files are opened (using binary write "wb" mode), and each output file's current byte count is stored in the dictionary out_sizes. 5. For each input file in descending size order, the code determines which output file currently is the smallest (using Python’s min() on out_sizes) and appends the content there. 6. Finally, the output files are closed. This approach should result in the output files being approximately equal in size.

Extract Code

I extracted the following code from that output: 

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#!/usr/bin/env python3
import os

def main():
    # Read the list of input file paths (one file per line)
    input_list_path = '/tmp/files'
    try:
        with open(input_list_path, 'r') as f:
            input_paths = [line.strip() for line in f if line.strip()]
    except Exception as e:
        print(f"Error opening {input_list_path}: {e}")
        return

    # Build a list of tuples (filepath, size) for all input files.
    files_with_size = []
    for path in input_paths:
        try:
            size = os.path.getsize(path)
            files_with_size.append((path, size))
        except OSError as e:
            print(f"Error getting size for {path}: {e} - Skipping.")
            continue

    # Sort the list by file size in descending order (largest first)
    files_with_size.sort(key=lambda x: x[1], reverse=True)

    # Prepare 16 output files, and keep track of their current size.
    num_outputs = 16
    out_fhs = {}
    out_sizes = {}

    for i in range(num_outputs):
        out_name = f"/tmp/merged_{i}"
        try:
            out_fhs[i] = open(out_name, 'wb')
            out_sizes[i] = 0
        except Exception as e:
            print(f"Error opening {out_name} for writing: {e}")
            for fh in out_fhs.values():
                fh.close()
            return

    # For each input file (largest first), append its content
    # to the output file that currently has the smallest size.
    for file_path, size in files_with_size:
        # Find output file index with smallest copied size so far.
        smallest_idx = min(out_sizes, key=out_sizes.get)
        try:
            with open(file_path, 'rb') as in_f:
                while True:
                    chunk = in_f.read(8192)
                    if not chunk:
                        break
                    out_fhs[smallest_idx].write(chunk)
            # Update the running total for that output.
            out_sizes[smallest_idx] += size
        except Exception as e:
            print(f"Error processing {file_path}: {e}")

    # Clean up: close all output files.
    for fh in out_fhs.values():
        fh.close()

if __name__ == '__main__':
    main()

Run Code Interpreter

Running the following program: 

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#!/usr/bin/env python3
import os

def main():
    # Read the list of input file paths (one file per line)
    input_list_path = '/tmp/files'
    try:
        with open(input_list_path, 'r') as f:
            input_paths = [line.strip() for line in f if line.strip()]
    except Exception as e:
        print(f"Error opening {input_list_path}: {e}")
        return

    # Build a list of tuples (filepath, size) for all input files.
    files_with_size = []
    for path in input_paths:
        try:
            size = os.path.getsize(path)
            files_with_size.append((path, size))
        except OSError as e:
            print(f"Error getting size for {path}: {e} - Skipping.")
            continue

    # Sort the list by file size in descending order (largest first)
    files_with_size.sort(key=lambda x: x[1], reverse=True)

    # Prepare 16 output files, and keep track of their current size.
    num_outputs = 16
    out_fhs = {}
    out_sizes = {}

    for i in range(num_outputs):
        out_name = f"/tmp/merged_{i}"
        try:
            out_fhs[i] = open(out_name, 'wb')
            out_sizes[i] = 0
        except Exception as e:
            print(f"Error opening {out_name} for writing: {e}")
            for fh in out_fhs.values():
                fh.close()
            return

    # For each input file (largest first), append its content
    # to the output file that currently has the smallest size.
    for file_path, size in files_with_size:
        # Find output file index with smallest copied size so far.
        smallest_idx = min(out_sizes, key=out_sizes.get)
        try:
            with open(file_path, 'rb') as in_f:
                while True:
                    chunk = in_f.read(8192)
                    if not chunk:
                        break
                    out_fhs[smallest_idx].write(chunk)
            # Update the running total for that output.
            out_sizes[smallest_idx] += size
        except Exception as e:
            print(f"Error processing {file_path}: {e}")

    # Clean up: close all output files.
    for fh in out_fhs.values():
        fh.close()

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

PyFunc

final: True Resulting in output: True