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question:# Terminal Control with Python's `tty` Module You are tasked with designing a small command-line tool in Python to interactively toggle the terminal mode between `raw` and `cbreak`. Requirements: 1. **Functions to Implement:** - `enable_raw_mode(fd: int) -> None`: This function should set the terminal to raw mode using the `tty.setraw` function. - `enable_cbreak_mode(fd: int) -> None`: This function should set the terminal to cbreak mode using the `tty.setcbreak` function. - `reset_terminal_mode(fd: int, original_attrs: list) -> None`: This function should reset the terminal to its original mode using `termios.tcsetattr`. - `toggle_modes(fd: int) -> None`: This function should toggle between `raw` and `cbreak` modes every 5 seconds until the user interrupts the program with a keyboard signal (e.g., CTRL+C). 2. **Input and Output Specifications:** - The file descriptor `fd` will always point to an open terminal (usually `sys.stdin.fileno()`). - The `reset_terminal_mode` function receives `original_attrs`, which is the initial terminal attributes returned by `termios.tcgetattr(fd)`. 3. **Constraints:** - The program should properly handle keyboard interrupts to exit gracefully and restore the terminal state to its original mode. - The functions must leverage the `tty` and `termios` modules together to manipulate terminal settings. 4. **Performance Requirements:** - Ensure the program is responsive to keyboard interrupts and restores the terminal's original settings before exiting. Example Usage: ```python import sys import termios import tty import time import signal def enable_raw_mode(fd: int) -> None: tty.setraw(fd) def enable_cbreak_mode(fd: int) -> None: tty.setcbreak(fd) def reset_terminal_mode(fd: int, original_attrs: list) -> None: termios.tcsetattr(fd, termios.TCSADRAIN, original_attrs) def toggle_modes(fd: int) -> None: original_attrs = termios.tcgetattr(fd) def signal_handler(sig, frame): reset_terminal_mode(fd, original_attrs) sys.exit(0) signal.signal(signal.SIGINT, signal_handler) try: while True: enable_raw_mode(fd) time.sleep(5) enable_cbreak_mode(fd) time.sleep(5) finally: reset_terminal_mode(fd, original_attrs) if __name__ == '__main__': fd = sys.stdin.fileno() toggle_modes(fd) ``` In this example, the `toggle_modes` function alternates the terminal mode between raw and cbreak every 5 seconds and restores the original terminal settings upon interruption.
answer:import sys import termios import tty import time import signal def enable_raw_mode(fd: int) -> None: Sets the terminal to raw mode. :param fd: File descriptor of the terminal tty.setraw(fd) def enable_cbreak_mode(fd: int) -> None: Sets the terminal to cbreak (canonical) mode. :param fd: File descriptor of the terminal tty.setcbreak(fd) def reset_terminal_mode(fd: int, original_attrs: list) -> None: Resets the terminal mode to the original settings. :param fd: File descriptor of the terminal :param original_attrs: Original terminal attributes to be restored termios.tcsetattr(fd, termios.TCSADRAIN, original_attrs) def toggle_modes(fd: int) -> None: Toggles between raw and cbreak modes every 5 seconds until interrupted by the user. :param fd: File descriptor of the terminal original_attrs = termios.tcgetattr(fd) def signal_handler(sig, frame): reset_terminal_mode(fd, original_attrs) sys.exit(0) signal.signal(signal.SIGINT, signal_handler) try: while True: enable_raw_mode(fd) time.sleep(5) enable_cbreak_mode(fd) time.sleep(5) finally: reset_terminal_mode(fd, original_attrs) if __name__ == '__main__': fd = sys.stdin.fileno() toggle_modes(fd)
question:<|Analysis Begin|> The provided documentation outlines the use of the `sns.diverging_palette` function in the seaborn library. This function is used to create diverging color palettes, primarily useful for visualizing data where the meaningful center point (often zero) splits the data into two contrasting values. The examples demonstrate: - Basic usage of generating a blue to red palette. - Modifying the central color to dark. - Returning a continuous colormap instead of a discrete palette. - Increasing separation around the central value. - Creating palettes with different color spectra (magenta-to-green). - Adjusting color properties like saturation and lightness of the endpoints. This documentation can help us design a question that assesses students' understanding of generating and customizing diverging palettes using seaborn. Students should be tasked with creating and manipulating such palettes and demonstrating their understanding through visual examples, combining various parameters available in `sns.diverging_palette`. <|Analysis End|> <|Question Begin|> # Coding Assessment Question: You are required to demonstrate your understanding of seaborn's `sns.diverging_palette` function by performing the following tasks: 1. **Generate and Display Basic Palettes**: - Create a basic diverging color palette that transitions from blue to red through white and display it. 2. **Modify Palettes with Custom Parameters**: - Create a diverging color palette with the central color set to dark. - Create another palette but modify it to be returned as a continuous colormap instead of a discrete palette. 3. **Advanced Customization**: - Create a diverging color palette (blue to red) with an increased amount of separation around the center value. - Create a magenta-to-green palette with reduced saturation of the endpoints. - Create a magenta-to-green diverging palette with reduced lightness of the endpoints. # Specifications: - **Input**: No user input is required; you will define all parameters within your code. - **Output**: Visual plot displaying the palettes. # Constraints: - Utilize seaborn's `sns.diverging_palette` function to generate color palettes. - Use matplotlib to display palettes if needed. - Ensure clarity by properly labeling each plot to describe what customization has been applied. # Example: ```python import seaborn as sns import matplotlib.pyplot as plt import numpy as np def display_palette(palette, title): sns.palplot(palette) plt.title(title) plt.show() # 1. Basic diverging palette palette1 = sns.diverging_palette(240, 20) display_palette(palette1, 'Basic Blue to Red Diverging Palette') # 2. Central color set to dark palette2 = sns.diverging_palette(240, 20, center="dark") display_palette(palette2, 'Diverging Palette with Dark Center') # 3. Continuous colormap palette3 = sns.diverging_palette(240, 20, as_cmap=True) sns.heatmap(np.random.rand(10, 10), cmap=palette3) plt.title('Continuous Diverging Colormap') plt.show() # 4. Increased separation around center palette4 = sns.diverging_palette(240, 20, sep=30) display_palette(palette4, 'Palette with Increased Separation') # 5. Magenta-to-green with reduced saturation palette5 = sns.diverging_palette(280, 150, s=50) display_palette(palette5, 'Magenta-to-Green with Reduced Saturation') # 6. Magenta-to-green with reduced lightness palette6 = sns.diverging_palette(280, 150, l=35) display_palette(palette6, 'Magenta-to-Green with Reduced Lightness') ``` By completing this question, you will demonstrate your ability to utilize seaborn for creating visually distinct and informative diverging color palettes with various custom parameters.
answer:import seaborn as sns import matplotlib.pyplot as plt import numpy as np def display_palette(palette, title): sns.palplot(palette) plt.title(title) plt.show() # 1. Basic diverging palette palette1 = sns.diverging_palette(240, 20) display_palette(palette1, 'Basic Blue to Red Diverging Palette') # 2. Central color set to dark palette2 = sns.diverging_palette(240, 20, center="dark") display_palette(palette2, 'Diverging Palette with Dark Center') # 3. Continuous colormap palette3 = sns.diverging_palette(240, 20, as_cmap=True) sns.heatmap(np.random.rand(10, 10), cmap=palette3) plt.title('Continuous Diverging Colormap') plt.show() # 4. Increased separation around center palette4 = sns.diverging_palette(240, 20, sep=30) display_palette(palette4, 'Palette with Increased Separation') # 5. Magenta-to-green with reduced saturation palette5 = sns.diverging_palette(280, 150, s=50) display_palette(palette5, 'Magenta-to-Green with Reduced Saturation') # 6. Magenta-to-green with reduced lightness palette6 = sns.diverging_palette(280, 150, l=35) display_palette(palette6, 'Magenta-to-Green with Reduced Lightness')
question:Publishing and Loading a Custom Model with PyTorch Hub Objective: You are required to create a `hubconf.py` file to publish a custom pre-trained model using PyTorch Hub. Subsequently, test the process of loading this model back using PyTorch Hub methods. Problem Statement: 1. Define a custom model using PyTorch's `nn.Module`. 2. Create a `hubconf.py` file that: - Specifies the dependencies required for the model. - Defines an entry point function to return an instance of the model with optional loading of pre-trained weights. 3. Simulate saving and loading a pre-trained state for your model. 4. Demonstrate loading the model from the hub and validating its functionality. Steps and Requirements: 1. **Custom Model Definition**: Define a simple custom neural network, e.g., an MLP (Multi-Layer Perceptron), using PyTorch's `nn.Module`. ```python import torch.nn as nn class CustomModel(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(CustomModel, self).__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.fc2 = nn.Linear(hidden_size, output_size) def forward(self, x): x = self.fc1(x) x = self.relu(x) x = self.fc2(x) return x ``` 2. **Creating `hubconf.py`**: - Specify the dependency on PyTorch. - Create an entry point function that optionally loads pre-trained weights. ```python dependencies = ['torch'] def custom_model(pretrained=False, **kwargs): Custom MLP Model. pretrained (bool): Load pretrained weights into the model. model = CustomModel(**kwargs) if pretrained: # Mock downloading pre-trained weights pretrained_dict = { "fc1.weight": torch.randn(kwargs['hidden_size'], kwargs['input_size']), "fc1.bias": torch.randn(kwargs['hidden_size']), "fc2.weight": torch.randn(kwargs['output_size'], kwargs['hidden_size']), "fc2.bias": torch.randn(kwargs['output_size']) } model.load_state_dict(pretrained_dict) return model ``` 3. **Simulating Saving and Loading Pre-trained State**: - Manually create and save the state dictionary for the `CustomModel` (this step can be mocked for instructional purposes). 4. **Loading the Model**: - Ensure the model can be loaded using `torch.hub.load()` and validate its functionality. ```python import torch # Simulate loading a model from the hub model = torch.hub.load('your_github-repo', 'custom_model', pretrained=True, input_size=10, hidden_size=20, output_size=1) print(model) ``` Constraints: - Ensure that your code runs successfully without any external dependencies beyond those specified. - Provide clear and concise documentation within your code. Performance Requirements: - Your model and entry functions should be designed to load efficiently within 1-2 seconds when using small input sizes. Submission: - Complete Python code defining the custom model, `hubconf.py`, and testing the loading of the model. - Submit all code files in a .zip archive.
answer:import torch import torch.nn as nn class CustomModel(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(CustomModel, self).__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.fc2 = nn.Linear(hidden_size, output_size) def forward(self, x): x = self.fc1(x) x = self.relu(x) x = self.fc2(x) return x # Simulating 'hubconf.py' content dependencies = ['torch'] def custom_model(pretrained=False, **kwargs): Custom MLP Model. pretrained (bool): Load pretrained weights into the model. model = CustomModel(**kwargs) if pretrained: # Mock downloading pre-trained weights pretrained_dict = { "fc1.weight": torch.randn(kwargs['hidden_size'], kwargs['input_size']), "fc1.bias": torch.randn(kwargs['hidden_size']), "fc2.weight": torch.randn(kwargs['output_size'], kwargs['hidden_size']), "fc2.bias": torch.randn(kwargs['output_size']) } model.load_state_dict(pretrained_dict) return model
question:# Unix Shadow Password Database Analysis You are required to create a Python function that parses and analyzes the Unix shadow password database entries, using the deprecated `spwd` module. The main goal is to check for users with passwords that are close to expiry and return their login names. Function Implementation ```python def find_users_with_expiring_passwords(warn_days): Return the list of users whose passwords will expire within the next `warn_days` days. Parameters: warn_days (int): The warning threshold in days before the password expiry to alert users. Returns: List[str]: A list of login names with expiring passwords within `warn_days` days. Raises: PermissionError: If the user does not have the necessary privileges to access the shadow password database. ``` Input and Output - **Input**: An integer `warn_days` representing the threshold in days. - **Output**: A list of strings, each being a login name of a user whose password is set to expire within `warn_days` days. Constraints - You must use the `spwd` module to retrieve the necessary data. - Handle any potential `PermissionError` exceptions by re-raising them. Performance Requirements - The function should efficiently handle the retrieval and checking of shadow password entries, even for large databases. Example ```python result = find_users_with_expiring_passwords(7) print(result) # Output might be: ['user1', 'user2'] ``` Note: You will likely need to test this in an environment where you have the necessary privileges to access the `/etc/shadow` file. Additional Information - Utilize the `spwd.getspall()` function to retrieve all shadow password entries. - Each entry is a tuple with `sp_expire` indicating the expiration date of the account. - Use the current date and the `sp_max` field (maximum number of days between changes) to determine the expiration date of each password. You should ensure your function is well-documented and handles exceptions as described.
answer:import spwd import time from datetime import datetime, timedelta def find_users_with_expiring_passwords(warn_days): Return the list of users whose passwords will expire within the next `warn_days` days. Parameters: warn_days (int): The warning threshold in days before the password expiry to alert users. Returns: List[str]: A list of login names with expiring passwords within `warn_days` days. Raises: PermissionError: If the user does not have the necessary privileges to access the shadow password database. try: shadow_entries = spwd.getspall() except PermissionError: raise PermissionError("Access to the shadow password database is required.") warning_users = [] current_date = datetime.now() for entry in shadow_entries: # Calculate expiration date from last change date and maximum password age if entry.sp_max == -1: # No expiration continue last_change_date = datetime.fromtimestamp(entry.sp_lstchg * 86400) expiration_date = last_change_date + timedelta(days=entry.sp_max) # Calculate remaining days till expiration days_remaining = (expiration_date - current_date).days if 0 <= days_remaining <= warn_days: warning_users.append(entry.sp_namp) return warning_users