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question:# Abstract Base Class Implementation and Virtual Subclasses Registration You are tasked with designing a library system where various types of media can be checked out by users. A media item must provide the following methods: 1. `check_out(self, user: str)`: Records that the media item is checked out by the given user. 2. `check_in(self)`: Records that the media item is available again. 3. `is_checked_out(self) -> bool`: Returns whether the media item is currently checked out. We will have three types of media in our system: `Book`, `DVD`, and `Magazine`. Implement these media types by adhering to the following rules: 1. Create an abstract base class `Media` that enforces the existence of the three methods mentioned above using abstract methods. 2. Implement concrete classes `Book`, `DVD`, and `Magazine` derived from `Media`. 3. Each media type should have an additional attribute specific to that type: - `Book`: `author` (str) - `DVD`: `director` (str) - `Magazine`: `issue_number` (int) 4. Register `VHS` as a virtual subclass of `Media` without inheriting from it. Implement the `VHS` class with the required methods. # Implementation Details 1. Define the abstract base class `Media` using the `ABC` class or `ABCMeta` metaclass. 2. Implement the derived classes `Book`, `DVD`, and `Magazine`, ensuring they provide concrete implementations of all abstract methods. 3. Register the `VHS` class as a virtual subclass of `Media`. 4. Demonstrate the dynamic registration by writing additional code to: - Create instances of each media type and check their functionalities. - Validate that `issubclass(VHS, Media)` returns `True`. # Constraints - You may not use any libraries or frameworks beyond standard Python 3.10. - Ensure that the `VHS` class does not inherit from `Media`, but still satisfies the interface requirements. # Example Usage ```python # Define ABC and concrete classes here... # Dynamically register VHS as a virtual subclass Media.register(VHS) # Demonstrate functionalities book = Book(title="1984", author="George Orwell") dvd = DVD(title="Inception", director="Christopher Nolan") magazine = Magazine(title="Science Today", issue_number=42) vhs = VHS(title="The Lion King") print(issubclass(VHS, Media)) # Should print: True print(isinstance(vhs, Media)) # Should print: True book.check_out("Alice") print(book.is_checked_out()) # Should print: True book.check_in() print(book.is_checked_out()) # Should print: False # And similarly for DVD, Magazine, and VHS instances ```
answer:from abc import ABC, abstractmethod class Media(ABC): @abstractmethod def check_out(self, user: str): pass @abstractmethod def check_in(self): pass @abstractmethod def is_checked_out(self) -> bool: pass class Book(Media): def __init__(self, title: str, author: str): self.title = title self.author = author self.checked_out = False self.checked_out_by = None def check_out(self, user: str): self.checked_out = True self.checked_out_by = user def check_in(self): self.checked_out = False self.checked_out_by = None def is_checked_out(self) -> bool: return self.checked_out class DVD(Media): def __init__(self, title: str, director: str): self.title = title self.director = director self.checked_out = False self.checked_out_by = None def check_out(self, user: str): self.checked_out = True self.checked_out_by = user def check_in(self): self.checked_out = False self.checked_out_by = None def is_checked_out(self) -> bool: return self.checked_out class Magazine(Media): def __init__(self, title: str, issue_number: int): self.title = title self.issue_number = issue_number self.checked_out = False self.checked_out_by = None def check_out(self, user: str): self.checked_out = True self.checked_out_by = user def check_in(self): self.checked_out = False self.checked_out_by = None def is_checked_out(self) -> bool: return self.checked_out class VHS: def __init__(self, title: str): self.title = title self.checked_out = False self.checked_out_by = None def check_out(self, user: str): self.checked_out = True self.checked_out_by = user def check_in(self): self.checked_out = False self.checked_out_by = None def is_checked_out(self) -> bool: return self.checked_out # Register VHS as a virtual subclass of Media Media.register(VHS)
question:# PyTorch Coding Assessment Question Objective This task assesses your understanding of the numerical properties in PyTorch using `torch.finfo` and `torch.iinfo`. Problem Statement You are required to implement a function `get_tensor_properties` in PyTorch, which calculates and returns a dictionary capturing various numerical properties of each data type for given tensors. Function Signature ```python def get_tensor_properties(tensor_list: List[torch.Tensor]) -> Dict[str, Dict[str, Union[int, float]]]: ``` Input: - `tensor_list`: A list of PyTorch tensors of various data types. Output: - A dictionary where keys are the data types of the tensors in string format and values are dictionaries containing the following numerical properties: - For floating-point tensors (i.e., checking types `torch.float32`, `torch.float64`, `torch.float16`, `torch.bfloat16`): - `bits`: The number of bits occupied by the type. - `eps`: The smallest representable number such that `1.0 + eps != 1.0`. - `max`: The largest representable number. - `min`: The smallest representable number (typically `-max`). - `tiny`: The smallest positive normal number. - For integer tensors (i.e., checking types `torch.uint8`, `torch.int8`, `torch.int16`, `torch.int32`, `torch.int64`): - `bits`: The number of bits occupied by the type. - `max`: The largest representable number. - `min`: The smallest representable number. Constraints: - The input list `tensor_list` can have from 1 to 100 tensors. - The tensors can have any shape. - The data types within the tensors will be one of the supported types listed above. Example: ```python import torch tensor_list = [ torch.tensor([1, 2, 3], dtype=torch.float32), torch.tensor([4, 5, 6], dtype=torch.int32) ] properties = get_tensor_properties(tensor_list) print(properties) ``` Expected Output: ```python { 'torch.float32': { 'bits': 32, 'eps': 1.1920928955078125e-07, 'max': 3.4028234663852886e+38, 'min': -3.4028234663852886e+38, 'tiny': 1.1754943508222875e-38, }, 'torch.int32': { 'bits': 32, 'max': 2147483647, 'min': -2147483648, } } ``` Notes: - Use `torch.finfo` and `torch.iinfo` to retrieve the numerical properties. - Consider the efficiency of your implementation. You may assume that input dtypes will not mix floating-point with integer types in a single tensor.
answer:import torch from typing import List, Dict, Union def get_tensor_properties(tensor_list: List[torch.Tensor]) -> Dict[str, Dict[str, Union[int, float]]]: properties = {} for tensor in tensor_list: dtype_str = str(tensor.dtype) if dtype_str not in properties: if tensor.dtype.is_floating_point: finfo = torch.finfo(tensor.dtype) properties[dtype_str] = { 'bits': finfo.bits, 'eps': finfo.eps, 'max': finfo.max, 'min': finfo.min, 'tiny': finfo.tiny, } else: iinfo = torch.iinfo(tensor.dtype) properties[dtype_str] = { 'bits': iinfo.bits, 'max': iinfo.max, 'min': iinfo.min, } return properties
question:# Memory Management in Python Objective: You are tasked with implementing a set of functions using Python's memory management functions to handle a dynamically allocated buffer. These functions must ensure that memory allocation and deallocation are correctly managed, particularly focusing on the `Mem` domain functions (`PyMem_*`). Requirements: 1. **Allocate a Buffer**: Implement a function that allocates memory for a given number of elements of a specified size, initialized to zero. 2. **Resize the Buffer**: Implement a function that resizes the allocated memory to a new size. 3. **Free the Buffer**: Implement a function that correctly frees the allocated memory. Functions to Implement: 1. `buffer_allocate(nelem, elsize):` - **Input**: - `nelem`: The number of elements to allocate. - `elsize`: The size of each element. - **Output**: A pointer to the allocated memory (simulated as an integer in Python, e.g., `id(memory)`). - **Constraints**: The function should use `PyMem_Calloc`. 2. `buffer_resize(pointer, new_size):` - **Input**: - `pointer`: The pointer to the currently allocated memory. - `new_size`: The new size to which the memory should be resized. - **Output**: A pointer to the resized memory (simulated as an integer in Python, e.g., `id(memory)`). - **Constraints**: The function should use `PyMem_Realloc`. 3. `buffer_free(pointer):` - **Input**: - `pointer`: The pointer to the allocated memory. - **Output**: None - **Constraints**: The function should use `PyMem_Free`. Example Usage: ```python buf_ptr = buffer_allocate(10, 4) print(f"Buffer allocated at: {buf_ptr}") buf_ptr = buffer_resize(buf_ptr, 20) print(f"Buffer resized at: {buf_ptr}") buffer_free(buf_ptr) print("Buffer freed") ``` Notes: 1. The Python memory manager is used under the hood for simulation purposes. 2. Use Python's `ctypes` library to simulate the behavior of memory pointers and allocation functions. 3. Ensure that your implementation correctly simulates allocation, resizing, and freeing of memory, mimicking the behavior of actual memory management functions.
answer:import ctypes def buffer_allocate(nelem, elsize): Allocate memory for a given number of elements of a specified size, initialized to zero. size = nelem * elsize buffer = ctypes.create_string_buffer(size) return ctypes.addressof(buffer) def buffer_resize(pointer, new_size): Resize the allocated memory to a new size. old_buffer = (ctypes.c_char * new_size).from_address(pointer) new_buffer = ctypes.create_string_buffer(new_size) ctypes.memmove(ctypes.addressof(new_buffer), old_buffer, new_size) return ctypes.addressof(new_buffer) def buffer_free(pointer): Free the allocated memory. # In ctypes, memory is managed automatically, so this is a no-op. # Leaving function implementation as an indicator of concept. pass
question:**Question: Dynamic Module Executor** You are tasked with creating a utility in Python that can dynamically execute Python modules or scripts from either the module namespace or filesystem path. The utility should perform the following tasks: 1. Accept a module name or a filesystem path as input. 2. Use the appropriate `runpy` function (`run_module` or `run_path`) to execute the provided module or script. 3. Collect and return the resulting module's globals dictionary after execution. 4. Ensure that any modifications to the `sys` module during execution are correctly reverted before the function returns. **Function Signature:** ```python def execute_dynamic_code(identifier: str, identifier_type: str, init_globals: dict = None) -> dict: pass ``` **Input:** - `identifier` (str): The name of the module or the filesystem path to the script that needs to be executed. - `identifier_type` (str): The type of identifier being provided. It can be either "module" for a module name or "path" for a filesystem path. - `init_globals` (dict): A dictionary of initial global variables to be passed to the executing code (default is `None`). **Output:** - A dictionary containing the global variables after executing the specified module or script. **Constraints:** - The provided `identifier_type` must be either "module" or "path". - The module name or path provided must exist and be valid. - The function should handle any exceptions that occur during execution and return an appropriate error message in the dictionary. **Example Usage:** ```python # Execute a module by name result = execute_dynamic_code('mymodule', 'module') print(result) # Execute a script from the filesystem path result = execute_dynamic_code('/path/to/myscript.py', 'path') print(result) ``` **Performance Requirements:** - The function should not leave any side effects, such as altered `sys` module states, after execution ends. - The function must handle both normal modules and package modules correctly. Implement the `execute_dynamic_code` function as described and ensure it meets the input and output specifications.
answer:import runpy import sys def execute_dynamic_code(identifier: str, identifier_type: str, init_globals: dict = None) -> dict: Executes a Python module or script dynamically from the specified module namespace or filesystem path. Args: - identifier (str): The module name or the filesystem path to the script. - identifier_type (str): The type of identifier ("module" or "path"). - init_globals (dict): A dictionary of initial global variables to be passed to the executing code (default is None). Returns: - dict: A dictionary containing the global variables after executing the specified module or script. # Validate identifier_type if identifier_type not in ("module", "path"): return {"error": "Invalid identifier_type. Must be 'module' or 'path'."} # Setup initial globals if not provided if init_globals is None: init_globals = {} saved_sys_argv = sys.argv[:] saved_sys_path = sys.path[:] try: if identifier_type == "module": result_globals = runpy.run_module(identifier, init_globals=init_globals, run_name="__main__") elif identifier_type == "path": sys.argv[0] = identifier result_globals = runpy.run_path(identifier, init_globals=init_globals) except Exception as e: return {"error": str(e)} finally: sys.argv = saved_sys_argv sys.path = saved_sys_path return result_globals