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

Here's a Python script that should accomplish the task you described: 

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import os
import math

# Function to get file size
def get_file_size(file_path):
    try:
        return os.path.getsize(file_path)
    except FileNotFoundError:
        return 0

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

# Sort the file paths by file size (largest first)
file_paths.sort(key=get_file_size, reverse=True)

# Create 16 empty files
merged_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]

# Iterate over the sorted file paths
for file_path in file_paths:
    try:
        with open(file_path, "rb") as f:
            file_content = f.read()
        # Find the smallest merged file and append the content
        min_size = min(len(merged_file.getvalue()) for merged_file in merged_files)
        min_file = [merged_file for merged_file in merged_files if len(merged_file.getvalue()) == min_size][0]
        min_file.write(file_content)
    except FileNotFoundError:
        pass

# Close all the merged files
for merged_file in merged_files:
    merged_file.close()
Here's how the script works: 1. The `get_file_size` function is a helper function that returns the size of a file, or `0` if the file is not found. 2. The script reads the file paths from `/tmp/files` and stores them in the `file_paths` list. 3. The `file_paths` list is sorted by file size in descending order using the `sort` method with a custom `key` function `get_file_size`. 4. 16 empty files are created with the names `/tmp/merged_0`, `/tmp/merged_1`, ..., `/tmp/merged_15`. 5. The script iterates over the sorted `file_paths` list. 6. For each file path, the script tries to open the file and read its content. 7. If the file is found, the script finds the smallest merged file (the one with the least amount of data) and appends the content of the current file to that merged file. 8. If the file is not found, the script skips it and moves to the next file path. 9. After processing all file paths, the script closes all the merged files. Note that this script assumes that the files in `/tmp/files` are paths to existing files. If some of the paths are invalid or point to non-existent files, the script will skip those paths and continue with the next valid file path.

Extract Code

I extracted the following code from that output: 

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import os
import math

# Function to get file size
def get_file_size(file_path):
    try:
        return os.path.getsize(file_path)
    except FileNotFoundError:
        return 0

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

# Sort the file paths by file size (largest first)
file_paths.sort(key=get_file_size, reverse=True)

# Create 16 empty files
merged_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]

# Iterate over the sorted file paths
for file_path in file_paths:
    try:
        with open(file_path, "rb") as f:
            file_content = f.read()
        # Find the smallest merged file and append the content
        min_size = min(len(merged_file.getvalue()) for merged_file in merged_files)
        min_file = [merged_file for merged_file in merged_files if len(merged_file.getvalue()) == min_size][0]
        min_file.write(file_content)
    except FileNotFoundError:
        pass

# Close all the merged files
for merged_file in merged_files:
    merged_file.close()

Run Code Interpreter

Running the following program: 

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import os
import math

# Function to get file size
def get_file_size(file_path):
    try:
        return os.path.getsize(file_path)
    except FileNotFoundError:
        return 0

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

# Sort the file paths by file size (largest first)
file_paths.sort(key=get_file_size, reverse=True)

# Create 16 empty files
merged_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]

# Iterate over the sorted file paths
for file_path in file_paths:
    try:
        with open(file_path, "rb") as f:
            file_content = f.read()
        # Find the smallest merged file and append the content
        min_size = min(len(merged_file.getvalue()) for merged_file in merged_files)
        min_file = [merged_file for merged_file in merged_files if len(merged_file.getvalue()) == min_size][0]
        min_file.write(file_content)
    except FileNotFoundError:
        pass

# Close all the merged files
for merged_file in merged_files:
    merged_file.close()
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 28, in <module> min_size = min(len(merged_file.getvalue()) for merged_file in merged_files) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 28, in <genexpr> min_size = min(len(merged_file.getvalue()) for merged_file in merged_files) ^^^^^^^^^^^^^^^^^^^^ AttributeError: '_io.BufferedWriter' object has no attribute 'getvalue'

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 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

Here's a Python script that should merge all the files listed in `/tmp/files` into 16 files of approximately equal size, sorted by file size in descending order: 

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import os
import glob
import math

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

# Get the total size of all files
total_size = sum(os.path.getsize(path) for path in file_paths)

# Calculate the target size for each output file
target_size = total_size // 16

# Sort the file paths by file size in descending order
file_paths.sort(key=lambda path: os.path.getsize(path), reverse=True)

# Initialize 16 output files
output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
sizes = [0] * 16
current_file = 0

# Merge the files
for path in file_paths:
    file_size = os.path.getsize(path)
    if sizes[current_file] + file_size > target_size:
        current_file = (current_file + 1) % 16
    with open(path, 'rb') as f:
        content = f.read()
        output_files[current_file].write(content)
        sizes[current_file] += file_size

# Close the output files
for f in output_files:
    f.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 the target size for each output file (`total_size // 16`). 3. It sorts the file paths by file size in descending order. 4. It initializes 16 output files `/tmp/merged_0` to `/tmp/merged_15`. 5. It iterates over the sorted file paths and appends the content of each file to the smallest output file that can accommodate it without exceeding the target size. 6. Finally, it closes all the output files. Note that this script assumes that the total size of all files is divisible by 16. If this is not the case, some output files may be slightly larger or smaller than the target size.

Extract Code

I extracted the following code from that output: 

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import os
import glob
import math

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

# Get the total size of all files
total_size = sum(os.path.getsize(path) for path in file_paths)

# Calculate the target size for each output file
target_size = total_size // 16

# Sort the file paths by file size in descending order
file_paths.sort(key=lambda path: os.path.getsize(path), reverse=True)

# Initialize 16 output files
output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
sizes = [0] * 16
current_file = 0

# Merge the files
for path in file_paths:
    file_size = os.path.getsize(path)
    if sizes[current_file] + file_size > target_size:
        current_file = (current_file + 1) % 16
    with open(path, 'rb') as f:
        content = f.read()
        output_files[current_file].write(content)
        sizes[current_file] += file_size

# Close the output files
for f in output_files:
    f.close()

Run Code Interpreter

Running the following program: 

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import os
import glob
import math

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

# Get the total size of all files
total_size = sum(os.path.getsize(path) for path in file_paths)

# Calculate the target size for each output file
target_size = total_size // 16

# Sort the file paths by file size in descending order
file_paths.sort(key=lambda path: os.path.getsize(path), reverse=True)

# Initialize 16 output files
output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
sizes = [0] * 16
current_file = 0

# Merge the files
for path in file_paths:
    file_size = os.path.getsize(path)
    if sizes[current_file] + file_size > target_size:
        current_file = (current_file + 1) % 16
    with open(path, 'rb') as f:
        content = f.read()
        output_files[current_file].write(content)
        sizes[current_file] += file_size

# Close the output files
for f in output_files:
    f.close()
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

Here's a Python script that should accomplish the task you've described: 

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import os
import math
import shutil

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

# Sort the file paths by file size (largest first)
file_paths.sort(key=lambda x: os.path.getsize(x), reverse=True)

# Create 16 empty files in /tmp/
for i in range(16):
    with open(f"/tmp/merged_{i}", "w") as f:
        pass

# Merge the files
for file_path in file_paths:
    # Find the smallest output file
    smallest_file = min([f"/tmp/merged_{i}" for i in range(16)], key=lambda x: os.path.getsize(x))

    # Append the content of the current file to the smallest output file
    with open(file_path, "r") as f_in, open(smallest_file, "a") as f_out:
        f_out.write(f_in.read())

# Print the sizes of the merged files
for i in range(16):
    merged_file = f"/tmp/merged_{i}"
    size = os.path.getsize(merged_file)
    print(f"{merged_file}: {size} bytes")
Here's how the script works: 1. The file paths from `/tmp/files` are read into a list `file_paths`. 2. The `file_paths` list is sorted in descending order of file size using the `sort` method with a custom `key` function that returns the file size for each path. 3. 16 empty files are created in `/tmp/` with names `/tmp/merged_0`, `/tmp/merged_1`, ..., `/tmp/merged_15`. 4. The script iterates over the sorted `file_paths` list. 5. For each file path, the script finds the smallest output file (`/tmp/merged_*`) by size using the `min` function with a generator expression. 6. The content of the current file is appended to the smallest output file using file operations. 7. Finally, the script prints the sizes of the 16 merged files. Note that this script assumes that the input files are not too large to fit in memory when reading their content. If you have very large files, you may need to modify the script to read and write the files in chunks.

Extract Code

I extracted the following code from that output: 

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import os
import math
import shutil

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

# Sort the file paths by file size (largest first)
file_paths.sort(key=lambda x: os.path.getsize(x), reverse=True)

# Create 16 empty files in /tmp/
for i in range(16):
    with open(f"/tmp/merged_{i}", "w") as f:
        pass

# Merge the files
for file_path in file_paths:
    # Find the smallest output file
    smallest_file = min([f"/tmp/merged_{i}" for i in range(16)], key=lambda x: os.path.getsize(x))

    # Append the content of the current file to the smallest output file
    with open(file_path, "r") as f_in, open(smallest_file, "a") as f_out:
        f_out.write(f_in.read())

# Print the sizes of the merged files
for i in range(16):
    merged_file = f"/tmp/merged_{i}"
    size = os.path.getsize(merged_file)
    print(f"{merged_file}: {size} bytes")

Run Code Interpreter

Running the following program: 

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import os
import math
import shutil

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

# Sort the file paths by file size (largest first)
file_paths.sort(key=lambda x: os.path.getsize(x), reverse=True)

# Create 16 empty files in /tmp/
for i in range(16):
    with open(f"/tmp/merged_{i}", "w") as f:
        pass

# Merge the files
for file_path in file_paths:
    # Find the smallest output file
    smallest_file = min([f"/tmp/merged_{i}" for i in range(16)], key=lambda x: os.path.getsize(x))

    # Append the content of the current file to the smallest output file
    with open(file_path, "r") as f_in, open(smallest_file, "a") as f_out:
        f_out.write(f_in.read())

# Print the sizes of the merged files
for i in range(16):
    merged_file = f"/tmp/merged_{i}"
    size = os.path.getsize(merged_file)
    print(f"{merged_file}: {size} bytes")
And got the output: /tmp/merged_0: 38661 bytes /tmp/merged_1: 38662 bytes /tmp/merged_2: 38661 bytes /tmp/merged_3: 38661 bytes /tmp/merged_4: 38663 bytes /tmp/merged_5: 38660 bytes /tmp/merged_6: 38662 bytes /tmp/merged_7: 38664 bytes /tmp/merged_8: 38661 bytes /tmp/merged_9: 38660 bytes /tmp/merged_10: 38662 bytes /tmp/merged_11: 38663 bytes /tmp/merged_12: 38660 bytes /tmp/merged_13: 38660 bytes /tmp/merged_14: 38661 bytes /tmp/merged_15: 38662 bytes

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

Here's a Python script that should accomplish the task: 

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import os
import heapq

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

# Get file sizes
file_sizes = [(os.path.getsize(path), path) for path in file_paths]

# Sort file paths by size (largest first)
file_sizes.sort(reverse=True)

# Initialize output files
output_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]
output_file_sizes = [0] * 16

# Distribute files to output files
for size, path in file_sizes:
    # Find the output file with the smallest size
    min_index = output_file_sizes.index(min(output_file_sizes))

    # Write the file content to the output file
    with open(path, "rb") as f:
        content = f.read()
    output_files[min_index].write(content)

    # Update the output file size
    output_file_sizes[min_index] += size

# Close output files
for f in output_files:
    f.close()
Here's how the script works: 1. It reads the file paths from `/tmp/files` and stores them in the `file_paths` list. 2. It gets the file sizes for each file path and stores them in the `file_sizes` list as tuples of `(size, path)`. 3. It sorts the `file_sizes` list in descending order of file sizes. 4. It initializes 16 output files with names `/tmp/merged_0`, `/tmp/merged_1`, ..., `/tmp/merged_15`. 5. It iterates over the sorted `file_sizes` list and writes the content of each file to the output file with the smallest size so far. 6. After writing a file, it updates the size of the corresponding output file. 7. Finally, it closes all the output files. This approach ensures that the files are distributed as evenly as possible among the 16 output files, with the largest files being split first. The output files should be approximately the same size, with some variation due to the different file sizes in the input. Note: This script assumes that the files fit in memory. If you have extremely large files that cannot fit in memory, you'll need to modify the script to read and write files in chunks.

Extract Code

I extracted the following code from that output: 

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import os
import heapq

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

# Get file sizes
file_sizes = [(os.path.getsize(path), path) for path in file_paths]

# Sort file paths by size (largest first)
file_sizes.sort(reverse=True)

# Initialize output files
output_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]
output_file_sizes = [0] * 16

# Distribute files to output files
for size, path in file_sizes:
    # Find the output file with the smallest size
    min_index = output_file_sizes.index(min(output_file_sizes))

    # Write the file content to the output file
    with open(path, "rb") as f:
        content = f.read()
    output_files[min_index].write(content)

    # Update the output file size
    output_file_sizes[min_index] += size

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

Run Code Interpreter

Running the following program: 

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import os
import heapq

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

# Get file sizes
file_sizes = [(os.path.getsize(path), path) for path in file_paths]

# Sort file paths by size (largest first)
file_sizes.sort(reverse=True)

# Initialize output files
output_files = [open(f"/tmp/merged_{i}", "wb") for i in range(16)]
output_file_sizes = [0] * 16

# Distribute files to output files
for size, path in file_sizes:
    # Find the output file with the smallest size
    min_index = output_file_sizes.index(min(output_file_sizes))

    # Write the file content to the output file
    with open(path, "rb") as f:
        content = f.read()
    output_files[min_index].write(content)

    # Update the output file size
    output_file_sizes[min_index] += size

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

Here's a Python script that should accomplish the task you've described: 

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import os
from pathlib import Path
from collections import deque

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

# Get file sizes and sort by size in descending order
file_sizes = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize output files
output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
output_sizes = [0] * 16
output_queue = deque(enumerate(output_files))

# Merge files
for path, size in file_sizes:
    with open(path, 'rb') as f:
        data = f.read()
    min_idx, min_file = output_queue.popleft()
    min_file.write(data)
    output_sizes[min_idx] += size
    output_queue.append((min_idx, min_file))

# Close output files
for f in output_files:
    f.close()
Here's how the script works: 1. The script reads the file paths from `/tmp/files` and stores them in a list `file_paths`. 2. It gets the file sizes for each file path and creates a list of tuples `(path, size)`. This list is then sorted in descending order of file sizes. 3. It initializes 16 output files `/tmp/merged_0` to `/tmp/merged_15` and a list `output_sizes` to keep track of the sizes of each output file. 4. A `deque` (double-ended queue) `output_queue` is created, which keeps track of the indices and file objects of the output files. 5. The script iterates over the sorted `file_sizes` list. For each file: - It reads the file data into memory. - It gets the index and file object of the smallest output file from `output_queue`. - It writes the file data to the smallest output file. - It updates the size of the smallest output file in `output_sizes`. - It puts the index and file object back into `output_queue`. 6. Finally, all output files are closed. This approach ensures that files are distributed across the 16 output files as evenly as possible, with the largest files being distributed first. The use of the `deque` helps in efficiently finding and updating the smallest output file at each step. Note: This script assumes that all input files can fit in memory simultaneously. If you have very large files or limited memory, you may need to modify the script to process files in chunks or use a different approach.

Extract Code

I extracted the following code from that output: 

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import os
from pathlib import Path
from collections import deque

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

# Get file sizes and sort by size in descending order
file_sizes = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize output files
output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
output_sizes = [0] * 16
output_queue = deque(enumerate(output_files))

# Merge files
for path, size in file_sizes:
    with open(path, 'rb') as f:
        data = f.read()
    min_idx, min_file = output_queue.popleft()
    min_file.write(data)
    output_sizes[min_idx] += size
    output_queue.append((min_idx, min_file))

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

Run Code Interpreter

Running the following program: 

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import os
from pathlib import Path
from collections import deque

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

# Get file sizes and sort by size in descending order
file_sizes = [(path, os.path.getsize(path)) for path in file_paths]
file_sizes.sort(key=lambda x: x[1], reverse=True)

# Initialize output files
output_files = [open(f'/tmp/merged_{i}', 'wb') for i in range(16)]
output_sizes = [0] * 16
output_queue = deque(enumerate(output_files))

# Merge files
for path, size in file_sizes:
    with open(path, 'rb') as f:
        data = f.read()
    min_idx, min_file = output_queue.popleft()
    min_file.write(data)
    output_sizes[min_idx] += size
    output_queue.append((min_idx, min_file))

# Close output files
for f in output_files:
    f.close()
And got the output:

PyFunc

final: False Resulting in output: False