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question:# Command Line Parser for a Mock File Processing Script Introduction You are to implement a command line parsing function for a mock file processing script using the `getopt` module. The script should accept various options and arguments as described below and handle them accordingly. Specifications 1. **Function Signature:** ```python def parse_arguments(argv: list) -> dict: ``` 2. **Input:** - `argv`: A list of command line arguments (emulating `sys.argv[1:]`), each element being a string. 3. **Output:** - A dictionary with parsed options and arguments. The dictionary should have the following structure: ```python { "help": bool, "verbose": bool, "output": str or None, "files": list of str } ``` 4. **Options:** - `-h` or `--help`: Display a help message and exit. If this option is found, populate the dictionary with `help: True` and terminate parsing. - `-v` or `--verbose`: Set verbosity. If this option is found, set `verbose: True`. - `-o <file>` or `--output=<file>`: Specify output file. Set `output: <file>` where `<file>` is the provided file name. - Remaining arguments are considered as files and should populate the `files` list in the output dictionary. 5. **Constraints:** - If an unknown option or a missing required argument is encountered, raise a `ValueError` with the message from the `getopt.GetoptError` exception. - Ensure the `output` is `None` if the output file is not specified. 6. **Example:** Input: ```python argv = ["-v", "--output", "result.txt", "file1", "file2"] ``` Output: ```python { "help": False, "verbose": True, "output": "result.txt", "files": ["file1", "file2"] } ``` Input: ```python argv = ["-h"] ``` Output: ```python { "help": True, "verbose": False, "output": None, "files": [] } ``` Implementation Notes: - Use the `getopt.getopt` function to parse the `argv`. - Handle and propagate `getopt.GetoptError` exceptions appropriately. Starter Code: ```python import getopt def parse_arguments(argv: list) -> dict: options = { "help": False, "verbose": False, "output": None, "files": [] } try: opts, files = getopt.getopt(argv, "hvo:", ["help", "verbose", "output="]) for opt, arg in opts: if opt in ("-h", "--help"): options["help"] = True return options elif opt in ("-v", "--verbose"): options["verbose"] = True elif opt in ("-o", "--output"): options["output"] = arg options["files"] = files except getopt.GetoptError as err: raise ValueError(str(err)) return options ``` Implement the function in the starter code template and test it against the provided examples.
answer:import getopt def parse_arguments(argv: list) -> dict: options = { "help": False, "verbose": False, "output": None, "files": [] } try: opts, files = getopt.getopt(argv, "hvo:", ["help", "verbose", "output="]) for opt, arg in opts: if opt in ("-h", "--help"): options["help"] = True return options elif opt in ("-v", "--verbose"): options["verbose"] = True elif opt in ("-o", "--output"): options["output"] = arg options["files"] = files except getopt.GetoptError as err: raise ValueError(str(err)) return options
question:# Task: Memory Usage Analysis using `tracemalloc` You are provided with a Python script that has potential memory inefficiencies. Your task is to write a function, `analyze_memory_usage`, to analyze the memory usage of this script using the `tracemalloc` module and identify memory leaks or inefficient memory usage. Below is the given script: ```python def inefficient_function(): large_list = [] for i in range(100000): large_list.append(i) return sum(large_list) def another_inefficient_function(): small_list = [i for i in range(1000)] return sum(small_list) ``` Function to Implement ```python def analyze_memory_usage(): # Your code goes here ``` The `analyze_memory_usage` function should: 1. **Start the memory tracing** with a limit of 25 frames. 2. **Run** the provided script to ensure memory allocation takes place. 3. **Take two snapshots**: `snapshot1` before running any functions and `snapshot2` after running both `inefficient_function` and `another_inefficient_function`. 4. **Compare** the two snapshots and find the top 5 sources of memory allocations. 5. **Filter out** any allocations not from the script file. 6. **Print** the results in a human-readable format. Example Output: ```text Top 5 allocations: 1. script.py:3: 1000 KiB large_list.append(i) 2. script.py:9: 200 KiB small_list = [i for i in range(1000)] ... ``` Notes: - Use `tracemalloc` and ensure proper start and stop calls. - Consider using filtering to precisely narrow down the memory allocation to specific lines in the script. - Ensure the output is sorted by the size of the allocations. - Validate that your function works correctly with other scripts that you might create for testing purposes. Good luck, and make sure your solution is efficient and clear!
answer:import tracemalloc def analyze_memory_usage(script): # Start tracing memory allocations tracemalloc.start(25) snapshot1 = tracemalloc.take_snapshot() # Run the script exec(script) snapshot2 = tracemalloc.take_snapshot() # Stop tracing to free memory tracemalloc.stop() # Get the difference between both snapshots top_stats = snapshot2.compare_to(snapshot1, 'lineno') print("Top 5 memory allocations:") for stat in top_stats[:5]: print(f"{stat}") # Example usage with the provided script script = def inefficient_function(): large_list = [] for i in range(100000): large_list.append(i) return sum(large_list) def another_inefficient_function(): small_list = [i for i in range(1000)] return sum(small_list) inefficient_function() another_inefficient_function() # Run the memory analysis analyze_memory_usage(script)
question:# Advanced PyTorch Coding Assessment Objective Your task is to implement a function using PyTorch that demonstrates your understanding of tensor operations and memory management. Problem Statement You need to: 1. Create a large PyTorch tensor. 2. Check the memory stats using the `torch.mtia.memory.memory_stats` function. 3. Perform multiple in-place operations on this tensor. 4. Check the memory stats again after performing the operations. 5. Return the memory stats before and after the operations for comparison. Specifications - You will implement a function `analyze_memory_stats` which takes the size of the tensor as an input parameter. - The input parameter `tensor_size` is a tuple representing the shape of the tensor (e.g., `(1000, 1000)`). Constraints - You must use in-place tensor operations to modify the tensor. - Ensure efficient memory usage throughout the operations. Input - `tensor_size` (tuple): A tuple of integers representing the shape of the tensor to be created. Output - A tuple containing two dictionaries: - The first dictionary contains memory stats before the in-place operations. - The second dictionary contains memory stats after the in-place operations. Example ```python import torch from torch.mtia.memory import memory_stats def analyze_memory_stats(tensor_size): # Create a large tensor with the given size tensor = torch.rand(tensor_size) # Get initial memory stats initial_stats = memory_stats() # Perform in-place operations on the tensor tensor.mul_(2) tensor.add_(5) tensor.sub_(3) # Get memory stats after operations final_stats = memory_stats() return (initial_stats, final_stats) # Example usage tensor_size = (1000, 1000) initial_stats, final_stats = analyze_memory_stats(tensor_size) print("Initial Stats:", initial_stats) print("Final Stats:", final_stats) ``` Notes - Ensure that `torch.mtia.memory.memory_stats` is properly imported and used. - The example provided should be tested and adapted to fit the actual `memory_stats` function behavior and output format.
answer:import torch def analyze_memory_stats(tensor_size): Analyze memory stats before and after performing in-place operations on a tensor. Parameters: tensor_size (tuple): The shape of the tensor to create. Returns: tuple: A tuple containing two dictionaries - memory stats before and after the operations. # Create a large tensor with the given size tensor = torch.rand(tensor_size) # Get initial memory stats initial_allocated = torch.cuda.memory_allocated() initial_reserved = torch.cuda.memory_reserved() # Perform in-place operations on the tensor tensor.mul_(2) tensor.add_(5) tensor.sub_(3) # Get memory stats after operations final_allocated = torch.cuda.memory_allocated() final_reserved = torch.cuda.memory_reserved() initial_stats = {"memory_allocated": initial_allocated, "memory_reserved": initial_reserved} final_stats = {"memory_allocated": final_allocated, "memory_reserved": final_reserved} return (initial_stats, final_stats)
question:# Python Typing and Generics Objective Implement a small library that leverages Python's "typing" module. This library will handle registering users and validating their data. The purpose of this exercise is to assess your understanding of type hints, generics, and user-defined types. Instructions 1. Define a **User** class that includes the following attributes: - `username: str` - `email: str` - `age: int` - `is_active: bool` 2. Create a **UserRegistry** class utilizing generics and type hints. This class should: - Store users in a list. - Provide a method `add_user` to add a new user to the registry. - Provide a method `get_user_by_username` to retrieve a user based on their username. - Ensure that all data being added to the registry is of the correct type. 3. Implement a **validate_user_data** function outside the UserRegistry class that: - Takes a dictionary as input. - Validates that the dictionary contains valid data for a `User`. - Returns a boolean indicating the data's validity. Example ```python from typing import List, Generic, TypeVar, Dict # Define your type hints and classes here T = TypeVar('T', bound='User') class User: def __init__(self, username: str, email: str, age: int, is_active: bool): self.username = username self.email = email self.age = age self.is_active = is_active class UserRegistry(Generic[T]): def __init__(self): self.users: List[T] = [] def add_user(self, user: T) -> None: self.users.append(user) def get_user_by_username(self, username: str) -> T: for user in self.users: if user.username == username: return user raise ValueError("User not found") def validate_user_data(data: Dict[str, any]) -> bool: required_keys = {"username", "email", "age", "is_active"} if not all(key in data for key in required_keys): return False if not isinstance(data["username"], str): return False if not isinstance(data["email"], str): return False if not isinstance(data["age"], int): return False if not isinstance(data["is_active"], bool): return False return True # Example usage registry = UserRegistry[User]() user_data = {"username": "john_doe", "email": "[email protected]", "age": 30, "is_active": True} if validate_user_data(user_data): user = User(**user_data) registry.add_user(user) retrieved_user = registry.get_user_by_username("john_doe") print(retrieved_user.username, retrieved_user.email, retrieved_user.age, retrieved_user.is_active) else: print("Invalid user data") ``` Constraints - Ensure the `UserRegistry` class enforces type hints and user generics appropriately. - The `validate_user_data` function should strictly check for the correct types specified. - Methods in the `UserRegistry` should handle cases where the user is not found gracefully. Notes - Code readability and proper use of type annotations are crucial. - Your implementation should aim to make adding and retrieving users intuitive and error-free.
answer:from typing import List, Generic, TypeVar, Dict # Define your type hints and classes here T = TypeVar('T', bound='User') class User: def __init__(self, username: str, email: str, age: int, is_active: bool): self.username = username self.email = email self.age = age self.is_active = is_active class UserRegistry(Generic[T]): def __init__(self): self.users: List[T] = [] def add_user(self, user: T) -> None: self.users.append(user) def get_user_by_username(self, username: str) -> T: for user in self.users: if user.username == username: return user raise ValueError("User not found") def validate_user_data(data: Dict[str, any]) -> bool: required_keys = {"username", "email", "age", "is_active"} if not all(key in data for key in required_keys): return False if not isinstance(data["username"], str): return False if not isinstance(data["email"], str): return False if not isinstance(data["age"], int): return False if not isinstance(data["is_active"], bool): return False return True