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question:Objective To assess your understanding of tensor storage in PyTorch, specifically focusing on `torch.UntypedStorage`. Problem Statement You are given two tensors, `tensor_a` and `tensor_b`. These tensors may or may not share the same underlying storage. Your task is to implement the following two functions: 1. `check_same_storage(tensor_a, tensor_b)`: This function should return `True` if `tensor_a` and `tensor_b` share the same underlying `UntypedStorage`, and `False` otherwise. 2. `fill_with_zeros(tensor, fill_grad=False)`: This function should fill the given `tensor` with zeros by directly modifying its underlying storage. If `fill_grad` is set to `True` and the tensor has `requires_grad=True`, also fill the gradient storage with zeros. Function Signature ```python def check_same_storage(tensor_a: torch.Tensor, tensor_b: torch.Tensor) -> bool: pass def fill_with_zeros(tensor: torch.Tensor, fill_grad: bool=False) -> None: pass ``` Input - `tensor_a` and `tensor_b`: Two PyTorch tensors. - `tensor`: A PyTorch tensor. - `fill_grad`: A boolean flag indicating whether to fill the gradient storage with zeros if the tensor has `requires_grad=True`. Output For `check_same_storage`: - Return `True` if the tensors share the same underlying `UntypedStorage`, otherwise return `False`. For `fill_with_zeros`: - The function directly modifies the tensor's storage and does not return anything. Constraints 1. You should not rely on specific tensor shapes or dtypes. 2. You must directly modify the underlying storage. 3. Follow best practices and avoid using deprecated storage manipulation methods when possible. Example ```python import torch # Example for check_same_storage tensor_a = torch.tensor([1.0, 2.0, 3.0]) tensor_b = tensor_a.view(3) assert check_same_storage(tensor_a, tensor_b) == True tensor_c = torch.tensor([1.0, 2.0, 3.0]) assert check_same_storage(tensor_a, tensor_c) == False # Example for fill_with_zeros tensor_d = torch.tensor([1.0, 2.0, 3.0]) fill_with_zeros(tensor_d) assert torch.equal(tensor_d, torch.tensor([0.0, 0.0, 0.0])) # Gradient fill example tensor_e = torch.tensor([1.0, 2.0, 3.0], requires_grad=True) tensor_e.sum().backward() # Creating a gradient fill_with_zeros(tensor_e, fill_grad=True) assert torch.equal(tensor_e, torch.tensor([0.0, 0.0, 0.0])) assert torch.equal(tensor_e.grad, torch.tensor([0.0, 0.0, 0.0])) ``` Use this coding question to demonstrate your mastery of tensor storage in PyTorch. Good luck!
answer:import torch def check_same_storage(tensor_a: torch.Tensor, tensor_b: torch.Tensor) -> bool: Check if tensor_a and tensor_b share the same underlying storage. Parameters: - tensor_a (torch.Tensor): First tensor. - tensor_b (torch.Tensor): Second tensor. Returns: - bool: True if the tensors share the same storage, False otherwise. return tensor_a.storage().data_ptr() == tensor_b.storage().data_ptr() def fill_with_zeros(tensor: torch.Tensor, fill_grad: bool=False) -> None: Fill the given tensor with zeros by directly modifying its underlying storage. Optionally fill the gradient storage with zeros if fill_grad is True. Parameters: - tensor (torch.Tensor): Tensor to be filled with zeros. - fill_grad (bool): Whether to also fill the gradient storage with zeros if tensor.requires_grad is True. Returns: - None tensor.storage().fill_(0) if fill_grad and tensor.requires_grad and tensor.grad is not None: tensor.grad.storage().fill_(0)
question:# Color Space Conversion Challenge Objective: The objective of this problem is to implement a series of functions that utilize the `colorsys` module to convert a given color between multiple color spaces and to calculate the average color in a list of colors, all using different color spaces. Task: You are required to implement the following functions: 1. **`convert_rgb_to_all(r, g, b)`**: This function should take RGB color values as input and return a dictionary containing the equivalent colors in YIQ, HLS, and HSV color spaces. - **Input**: - `r` (float): Red component of the color (0 <= r <= 1) - `g` (float): Green component of the color (0 <= g <= 1) - `b` (float): Blue component of the color (0 <= b <= 1) - **Output**: - Dictionary with keys `'YIQ'`, `'HLS'`, and `'HSV'` and their corresponding values being tuples of the converted color coordinates. Example: ```python { 'YIQ': (0.385, 0.124, 0.269), 'HLS': (0.5, 0.3, 0.2), 'HSV': (0.5, 0.25, 0.4) } ``` 2. **`average_color(colors)`**: This function should take a list of colors, each represented as a dictionary with keys `'r'`, `'g'`, `'b'`, and return the average color in RGB, HLS, and HSV formats. - **Input**: - `colors` (list): A list of dictionaries, each with keys `'r'`, `'g'`, and `'b'` representing the Red, Green, and Blue components of a color. Each value is a float between 0 and 1. Example: ```python [ {'r': 0.2, 'g': 0.4, 'b': 0.4}, {'r': 0.6, 'g': 0.7, 'b': 0.8} ] ``` - **Output**: - Dictionary with keys `'average_rgb'`, `'average_hls'`, and `'average_hsv'` containing the average color in each of these color spaces: Example: ```python { 'average_rgb': (0.4, 0.55, 0.6), 'average_hls': (0.44, 0.5, 0.25), 'average_hsv': (0.47, 0.33, 0.6) } ``` Constraints: 1. You may assume the input list for `average_color` will always have at least one color. 2. The functions should use the `colorsys` module for conversions. Evaluation: Your implementation will be evaluated based on: - Correctness of the color space conversions. - Correctness of the average color calculations. - Code readability and adherence to best practices. Example Usage: ```python print(convert_rgb_to_all(0.2, 0.4, 0.4)) # Output: {'YIQ': (0.38199999999999995, -0.15800000000000003, -0.07600000000000001), 'HLS': (0.5, 0.30000000000000004, 0.33333333333333337), 'HSV': (0.5, 0.5, 0.4)} colors = [ {'r': 0.2, 'g': 0.4, 'b': 0.4}, {'r': 0.6, 'g': 0.7, 'b': 0.8} ] print(average_color(colors)) # Output: {'average_rgb': (0.4, 0.55, 0.6000000000000001), 'average_hls': (0.5357142857142857, 0.5, 0.3), 'average_hsv': (0.5833333333333333, 0.5545454545454546, 0.6636363636363636)} ```
answer:import colorsys def convert_rgb_to_all(r, g, b): Converts RGB color values to YIQ, HLS, and HSV color spaces. Arguments: r, g, b -- RGB color values (0 <= r,g,b <= 1) Returns: A dictionary containing the YIQ, HLS, and HSV equivalents. yiq = colorsys.rgb_to_yiq(r, g, b) hls = colorsys.rgb_to_hls(r, g, b) hsv = colorsys.rgb_to_hsv(r, g, b) return { 'YIQ': yiq, 'HLS': hls, 'HSV': hsv } def average_color(colors): Calculates the average color of a list of RGB colors in RGB, HLS, and HSV color spaces. Arguments: colors -- a list of dictionaries with keys 'r', 'g', 'b' representing RGB color values (0 <= r,g,b <= 1) Returns: A dictionary containing the average color in RGB, HLS, and HSV color spaces. # Calculate average RGB avg_r = sum(color['r'] for color in colors) / len(colors) avg_g = sum(color['g'] for color in colors) / len(colors) avg_b = sum(color['b'] for color in colors) / len(colors) avg_rgb = (avg_r, avg_g, avg_b) # Convert average RGB to HLS and HSV avg_hls = colorsys.rgb_to_hls(avg_r, avg_g, avg_b) avg_hsv = colorsys.rgb_to_hsv(avg_r, avg_g, avg_b) return { 'average_rgb': avg_rgb, 'average_hls': avg_hls, 'average_hsv': avg_hsv }
question:Objective Design a function that creates a Seaborn visualization with a specific style and customized parameters. Problem Statement Create a function called `custom_barplot` that takes the following parameters: - `x`: a list of labels for the x-axis. - `y`: a list of values corresponding to the x-axis labels. - `style`: a string specifying the Seaborn style to apply (e.g., `whitegrid`, `darkgrid`). - `custom_params`: a dictionary of Seaborn style parameters to customize (e.g., `{"grid.color": ".5", "grid.linestyle": "--"}`). The function should: 1. Set the Seaborn style specified by the `style` parameter. 2. Apply any custom parameters provided in `custom_params`. 3. Create and display a bar plot using the `x` and `y` values. Input - `x`: List[str] (e.g., `["A", "B", "C"]`) - `y`: List[float] (e.g., `[1.0, 3.0, 2.0]`) - `style`: str (e.g., `"whitegrid"`) - `custom_params`: Dict (e.g., `{"grid.color": ".5", "grid.linestyle": "--"}`) Output - The function should not return anything, but should display the plot. Constraints - The lengths of `x` and `y` will be the same. - Valid styles are `darkgrid`, `whitegrid`, `dark`, `white`, and `ticks`. - `custom_params` keys will be valid Seaborn style parameters. Example ```python def custom_barplot(x, y, style, custom_params): import seaborn as sns import matplotlib.pyplot as plt # Set the specified style sns.set_style(style, custom_params) # Create barplot sns.barplot(x=x, y=y) # Display the plot plt.show() # Example usage x = ["A", "B", "C"] y = [1, 3, 2] style = "darkgrid" custom_params = {"grid.color": ".6", "grid.linestyle": ":"} custom_barplot(x, y, style, custom_params) ``` In this example, the function sets the `darkgrid` style with a customized grid color and linestyle, and then displays the bar plot based on the x and y values.
answer:import seaborn as sns import matplotlib.pyplot as plt def custom_barplot(x, y, style, custom_params): This function creates a Seaborn bar plot with the specified style and custom parameters. Parameters: x (list): a list of labels for the x-axis. y (list): a list of values corresponding to the x-axis labels. style (str): a string specifying the Seaborn style to apply. custom_params (dict): a dictionary of Seaborn style parameters to customize. Returns: None # Set the specified style sns.set_style(style, custom_params) # Create barplot sns.barplot(x=x, y=y) # Display the plot plt.show()
question:You are tasked with developing a web crawler that respects the rules set out in a website's `robots.txt` file. Your job is to use the `urllib.robotparser` module to determine if a web crawler (identified by its user agent) can access certain URLs on a specified website based on the site's `robots.txt` file. Problem Specification 1. **Function Name**: `check_access` 2. **Parameters**: - `robots_url` (str): The URL to the `robots.txt` file of the website. - `user_agent` (str): The user agent string identifying the web crawler. - `urls` (list of str): A list of URLs that you want to check for access permission. 3. **Returns**: - A dictionary where the keys are the URLs from the `urls` list and the values are boolean values (`True` if the user agent is allowed to access the URL, `False` otherwise). Example ```python def check_access(robots_url, user_agent, urls): # Your implementation here # Example usage: robots_url = "http://www.example.com/robots.txt" user_agent = "MyUserAgent" urls = [ "http://www.example.com/", "http://www.example.com/private/", "http://www.example.com/public/" ] result = check_access(robots_url, user_agent, urls) print(result) # Output might be: {"http://www.example.com/": True, "http://www.example.com/private/": False, "http://www.example.com/public/": True} ``` # Constraints 1. The `robots.txt` file must be read from the provided URL. 2. Parse the fetched `robots.txt` data to check the permissions. 3. You must handle potential network errors gracefully. # Performance Requirements Your solution should efficiently read and parse the `robots.txt` file and check access for each URL provided. Ensure that network operations are properly handled to avoid unnecessary delays.
answer:import urllib.robotparser import urllib.error def check_access(robots_url, user_agent, urls): Given the URL of a robots.txt file and a user agent string, determine if the user agent is allowed to access each URL in a list of URLs. Parameters: robots_url (str): The URL to the robots.txt file. user_agent (str): The user agent string identifying the web crawler. urls (list of str): A list of URLs to check for access permission. Returns: dict: A dictionary where the keys are URLs and the values are booleans indicating whether access is allowed. rp = urllib.robotparser.RobotFileParser() try: rp.set_url(robots_url) rp.read() except (urllib.error.URLError, IOError) as e: print(f"Error fetching robots.txt: {e}") return {url: False for url in urls} access_dict = {} for url in urls: access_dict[url] = rp.can_fetch(user_agent, url) return access_dict