DefinetlyNotAI/Logicytics

import os
import random
from logicytics import deprecated
from faker import Faker

MAX_FILE_SIZE: int = 10 * 1024  # Example: Max file size is 10 KB
SAVE_DIRECTORY: str = "PATH"
# Initialize the Faker instance
fake = Faker()


@deprecated(reason="This function is only used for generating sensitive data for testing purposes for v2 trainers, v2 trainers are deprecated now, use v3 trainers.", removal_version="3.4.0")
def create_sensitive_file(file_path: str, max_size: int):
    """
    Generate a sensitive file with real sensitive information.

    Args:
        file_path (str): The path where the file will be saved.
        max_size (int): The maximum size of the file in bytes.
    """
    content = ""
    # Generate sensitive data using Faker
    content += f"Name: {fake.name()}\n"
    content += f"Address: {fake.address()}\n"
    content += f"Phone: {fake.phone_number()}\n"
    content += f"Email: {fake.email()}\n"
    content += f"Credit Card: {fake.credit_card_number()}\n"
    content += f"SSN: {fake.ssn()}\n"
    content += f"Company: {fake.company()}\n"

    # Keep adding more sensitive data until the file size is less than the max limit
    while len(content.encode('utf-8')) < max_size:
        content += f"Sensitive Info: {fake.text(max_nb_chars=200)}\n"

    with open(file_path, "w", encoding="utf-8") as f:
        f.write(content)


@deprecated(reason="This function is only used for generating normal data for testing purposes for v2 trainers, v2 trainers are deprecated now, use v3 trainers.", removal_version="3.4.0")
def create_normal_file(file_path: str, max_size: int):
    """
    Generate a normal file with non-sensitive data.

    Args:
        file_path (str): The path where the file will be saved.
        max_size (int): The maximum size of the file in bytes.
    """
    content = ""
    # Add random text
    while len(content.encode('utf-8')) < max_size:
        content += fake.text(max_nb_chars=200) + "\n"

    with open(file_path, "w", encoding="utf-8") as f:
        f.write(content)


@deprecated(reason="This function is only used for generating mixed data for testing purposes for v2 trainers, v2 trainers are deprecated now, use v3 trainers.", removal_version="3.4.0")
def create_mix_file(file_path: str, max_size: int):
    """
    Generate a mix file with both normal and sensitive data.

    Args:
        file_path (str): The path where the file will be saved.
        max_size (int): The maximum size of the file in bytes.
    """
    content = ""
    # Add a mix of normal and sensitive data
    while len(content.encode('utf-8')) < max_size:
        if random.choice([True, False]):
            content += fake.text(max_nb_chars=200) + "\n"  # Normal data
        else:
            content += f"Name: {fake.name()}\n"
            content += f"Credit Card: {fake.credit_card_number()}\n"
            content += f"SSN: {fake.ssn()}\n"  # Sensitive data

    with open(file_path, "w", encoding="utf-8") as f:
        f.write(content)


@deprecated(reason="This function is only used for generating random files for testing purposes for v2 trainers, v2 trainers are deprecated now, use v3 trainers.", removal_version="3.4.0")
def create_random_files(directories: str, num_file: int = 100):
    """
    Create random files (Normal, Mix, Sensitive).

    Args:
        directories (str): The directory where the files will be saved.
        num_file (int): The number of files to generate.
    """
    os.makedirs(directories, exist_ok=True)

    for i in range(num_file):
        file_type = random.choice(['Normal', 'Mix', 'Sensitive'])
        file_name = f"file_{i + 1}_{file_type}.txt"
        file_path = os.path.join(directories, file_name)

        if file_type == "Sensitive":
            create_sensitive_file(file_path, MAX_FILE_SIZE)
        elif file_type == "Mix":
            create_mix_file(file_path, MAX_FILE_SIZE)
        else:
            create_normal_file(file_path, MAX_FILE_SIZE)

        print(f"Created {file_type} file: {file_name}")


if __name__ == "__main__":
    create_random_files(SAVE_DIRECTORY, num_file=1000000)
else:
    raise ImportError("This training script is meant to be run directly "
                      "and cannot be imported. Please execute it as a standalone script.")