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

Sure, I can help you draft a Python script for this task. The script will read the file paths from `/tmp/files`, open each file, read its content, and then write the content to one of the 16 output files in a round-robin fashion to ensure each output file is approximately the same size. Here's a simple script that should do the job: 

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# The number of output files
n_outputs = 16

# Open the input file and read the lines
with open('/tmp/files', 'r') as f:
    file_paths = f.readlines()

# Remove any newline characters from the file paths
file_paths = [path.strip() for path in file_paths]

# Initialize the output files
output_files = [open(f'/tmp/merged_{i}', 'w') for i in range(n_outputs)]

# Iterate over the input files
for i, path in enumerate(file_paths):
    # Choose the output file based on the index of the input file
    output_index = i % n_outputs

    # Open the input file and read its content
    with open(path, 'r') as f:
        content = f.read()

    # Write the content to the chosen output file
    output_files[output_index].write(content)

# Close all the output files
for f in output_files:
    f.close()
This script assumes that all the files listed in `/tmp/files` exist and are accessible. It also assumes that the files are small enough to be read into memory in their entirety. If the files are large, you may need to read and write them in chunks to avoid running out of memory. Please ensure to run this script in a Python 3 environment and replace `n_outputs` with the desired number of output files.

Extract Code

I extracted the following code from that output: 

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# The number of output files
n_outputs = 16

# Open the input file and read the lines
with open('/tmp/files', 'r') as f:
    file_paths = f.readlines()

# Remove any newline characters from the file paths
file_paths = [path.strip() for path in file_paths]

# Initialize the output files
output_files = [open(f'/tmp/merged_{i}', 'w') for i in range(n_outputs)]

# Iterate over the input files
for i, path in enumerate(file_paths):
    # Choose the output file based on the index of the input file
    output_index = i % n_outputs

    # Open the input file and read its content
    with open(path, 'r') as f:
        content = f.read()

    # Write the content to the chosen output file
    output_files[output_index].write(content)

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

Run Code Interpreter

Running the following program: 

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# The number of output files
n_outputs = 16

# Open the input file and read the lines
with open('/tmp/files', 'r') as f:
    file_paths = f.readlines()

# Remove any newline characters from the file paths
file_paths = [path.strip() for path in file_paths]

# Initialize the output files
output_files = [open(f'/tmp/merged_{i}', 'w') for i in range(n_outputs)]

# Iterate over the input files
for i, path in enumerate(file_paths):
    # Choose the output file based on the index of the input file
    output_index = i % n_outputs

    # Open the input file and read its content
    with open(path, 'r') as f:
        content = f.read()

    # Write the content to the chosen output file
    output_files[output_index].write(content)

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

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 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

Here's a Python script that reads the list of file paths from `/tmp/files` and merges their contents into 16 output files named `/tmp/merged_%d` (where %d is a number from 0 to 15). The script ensures that the output files have approximately the same size. 

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import os

chunk_count = 16
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(chunk_count)]

with open("/tmp/files") as input_file:
    file_paths = input_file.read().splitlines()

for file_path in file_paths:
    with open(file_path) as f:
        current_file_index = os.stat(file_path).st_size % chunk_count
        output_files[current_file_index].write(f.read())

for output_file in output_files:
    output_file.close()
This script uses the file size (in bytes) of each input file to determine which output file it should be written to. This is done using the modulo operator, which distributes the files evenly across the 16 output files. **Note**: Make sure to adjust the `chunk_count` variable to 16 or any other number of output files that you'd like. This script assumes that the input file paths in `/tmp/files` are valid and accessible. If there are any errors with the input file paths, you may need to add error handling to the script.

Extract Code

I extracted the following code from that output: 

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import os

chunk_count = 16
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(chunk_count)]

with open("/tmp/files") as input_file:
    file_paths = input_file.read().splitlines()

for file_path in file_paths:
    with open(file_path) as f:
        current_file_index = os.stat(file_path).st_size % chunk_count
        output_files[current_file_index].write(f.read())

for output_file in output_files:
    output_file.close()

Run Code Interpreter

Running the following program: 

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import os

chunk_count = 16
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(chunk_count)]

with open("/tmp/files") as input_file:
    file_paths = input_file.read().splitlines()

for file_path in file_paths:
    with open(file_path) as f:
        current_file_index = os.stat(file_path).st_size % chunk_count
        output_files[current_file_index].write(f.read())

for output_file in output_files:
    output_file.close()
And got the output:

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 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

You can use the following Python script to merge the files into 16 parts with approximately the same size. This script reads the file paths from "/tmp/files" and distributes their content into the 16 output files. 

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import os

chunk_count = 16
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(chunk_count)]

with open("/tmp/files") as file:
    file_paths = file.read().splitlines()

# Calculate the approximate number of files per chunk
files_per_chunk = len(file_paths) // chunk_count

for i, file_path in enumerate(file_paths):
    output_file_idx = i // files_per_chunk

    with open(file_path) as input_file:
        output_files[output_file_idx].write(input_file.read())

# Close the output files
for output_file in output_files:
    output_file.close()
This script assumes that the file paths in "/tmp/files" are one per line. It calculates the approximate number of input files per output file and writes the content of each input file to the corresponding output file. After processing all input files, the script closes the output files.

Extract Code

I extracted the following code from that output: 

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import os

chunk_count = 16
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(chunk_count)]

with open("/tmp/files") as file:
    file_paths = file.read().splitlines()

# Calculate the approximate number of files per chunk
files_per_chunk = len(file_paths) // chunk_count

for i, file_path in enumerate(file_paths):
    output_file_idx = i // files_per_chunk

    with open(file_path) as input_file:
        output_files[output_file_idx].write(input_file.read())

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

Run Code Interpreter

Running the following program: 

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import os

chunk_count = 16
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(chunk_count)]

with open("/tmp/files") as file:
    file_paths = file.read().splitlines()

# Calculate the approximate number of files per chunk
files_per_chunk = len(file_paths) // chunk_count

for i, file_path in enumerate(file_paths):
    output_file_idx = i // files_per_chunk

    with open(file_path) as input_file:
        output_files[output_file_idx].write(input_file.read())

# Close the output files
for output_file in output_files:
    output_file.close()
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 17, in <module> output_files[output_file_idx].write(input_file.read()) ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ IndexError: list index out of range

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 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

Sure, here's a Python script that should do what you're asking for. This script reads the file paths from "/tmp/files", reads the content of each file, and writes it to one of the 16 output files in a round-robin fashion to ensure each output file is approximately the same size. 

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import os

# List to hold the output file handles
output_files = []

# Open the output files
for i in range(16):
    output_file = open(f"/tmp/merged_{i}", "w")
    output_files.append(output_file)

# Read the input file paths
with open("/tmp/files", "r") as input_file:
    file_paths = input_file.readlines()

# Go through each file path
for file_path in file_paths:
    file_path = file_path.strip()

    # Read the file
    with open(file_path, "r") as file:
        content = file.read()

        # Write the content to the next output file
        output_files[i % 16].write(content)

# Close the output files
for output_file in output_files:
    output_file.close()
Please note that this script assumes that all files are small enough to be loaded into memory at once. If your files are large, you may need to read and write them in chunks to avoid running out of memory. Also, this script does not handle errors that may occur when opening files, such as if a file does not exist or if you don't have permission to read it. You may want to add error handling code to make the script more robust.

Extract Code

I extracted the following code from that output: 

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import os

# List to hold the output file handles
output_files = []

# Open the output files
for i in range(16):
    output_file = open(f"/tmp/merged_{i}", "w")
    output_files.append(output_file)

# Read the input file paths
with open("/tmp/files", "r") as input_file:
    file_paths = input_file.readlines()

# Go through each file path
for file_path in file_paths:
    file_path = file_path.strip()

    # Read the file
    with open(file_path, "r") as file:
        content = file.read()

        # Write the content to the next output file
        output_files[i % 16].write(content)

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

Run Code Interpreter

Running the following program: 

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import os

# List to hold the output file handles
output_files = []

# Open the output files
for i in range(16):
    output_file = open(f"/tmp/merged_{i}", "w")
    output_files.append(output_file)

# Read the input file paths
with open("/tmp/files", "r") as input_file:
    file_paths = input_file.readlines()

# Go through each file path
for file_path in file_paths:
    file_path = file_path.strip()

    # Read the file
    with open(file_path, "r") as file:
        content = file.read()

        # Write the content to the next output file
        output_files[i % 16].write(content)

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

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 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

Sure, I can help you draft a Python script for this task. The script will read the file paths from `/tmp/files`, open each file, read its content, and then write it to one of the 16 output files in a round-robin fashion to ensure each output file is approximately the same size. Here's a simple script that should do the job: 

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import os

# The number of output files you want
num_output_files = 16

# Open all output files in write mode
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(num_output_files)]

# Read the input file paths
with open("/tmp/files", "r") as input_file:
    for file_path in input_file:
        file_path = file_path.strip()
        if os.path.exists(file_path):
            # Open each file and read its content
            with open(file_path, "r") as file:
                content = file.read()

                # Write the content to the next output file in the round-robin sequence
                output_files[i % num_output_files].write(content)

                # Close the file after reading its content
                file.close()

# Close all output files
for output_file in output_files:
    output_file.close()
This script assumes that all files are small enough to fit into memory. If you have large files, you might want to read and write them in chunks to avoid running out of memory. Also, please ensure that you have the necessary permissions to read and write files in the specified directories.

Extract Code

I extracted the following code from that output: 

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import os

# The number of output files you want
num_output_files = 16

# Open all output files in write mode
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(num_output_files)]

# Read the input file paths
with open("/tmp/files", "r") as input_file:
    for file_path in input_file:
        file_path = file_path.strip()
        if os.path.exists(file_path):
            # Open each file and read its content
            with open(file_path, "r") as file:
                content = file.read()

                # Write the content to the next output file in the round-robin sequence
                output_files[i % num_output_files].write(content)

                # Close the file after reading its content
                file.close()

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

Run Code Interpreter

Running the following program: 

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import os

# The number of output files you want
num_output_files = 16

# Open all output files in write mode
output_files = [open(f"/tmp/merged_{i}", "w") for i in range(num_output_files)]

# Read the input file paths
with open("/tmp/files", "r") as input_file:
    for file_path in input_file:
        file_path = file_path.strip()
        if os.path.exists(file_path):
            # Open each file and read its content
            with open(file_path, "r") as file:
                content = file.read()

                # Write the content to the next output file in the round-robin sequence
                output_files[i % num_output_files].write(content)

                # Close the file after reading its content
                file.close()

# Close all output files
for output_file in output_files:
    output_file.close()
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 20, in <module> output_files[i % num_output_files].write(content) ^ NameError: name 'i' is not defined. Did you mean: 'id'?

PyFunc

final: False Resulting in output: False