Refactor Classes to Use Dataclasses for Improved Readability and Efficiency

[SZim92]

Description:

To enhance code readability, reduce boilerplate code, and improve maintainability, classes will be refactored to use Python's dataclasses module where appropriate. Dataclasses provide a decorator and functions for automatically adding special methods such as __init__(), __repr__(), and __eq__() to user-defined classes.

Objectives:

1. Identify key classes that can be refactored to use dataclasses.
2. Refactor identified classes to use the dataclass decorator.
3. Ensure all unit tests pass after the refactoring.

Tasks:

1. Identify Key Classes:

   • Review the codebase to identify classes that can benefit from being refactored to dataclasses.

2. Refactor to Dataclasses:

   • Add the @dataclass decorator to identified classes.
   • Remove boilerplate code that is automatically handled by dataclasses.
   • Ensure all class attributes are properly annotated with types.

3. Verify Changes:

   • Run all existing unit tests to ensure they pass.
   • Verify that the refactored classes behave as expected.

Implementation Plan:

1. Identify Classes to Refactor:

   • Review the codebase manually or use tools to identify classes that can be refactored to dataclasses.

2. Example Refactoring:

   • Current Code:

     class ModelDownloaderApi:
      def __init__(self):
          self.file_queue = list()
          self.fs = HfFileSystem()
          self.total_size = 0
     
      def get_info(self, repo_id: str, is_sd: bool = False) -> None:
          self.repo_id = repo_id
          self.repo_folder = repo_id.replace('/', '---')
          self.file_queue.clear()
          self.total_size = 0
          self.enum_file_list(repo_id, is_sd, True)
          print(dumps({"total_size": self.total_size, "file_list": self.file_queue}))

   • Refactored Code Using Dataclasses:

     from dataclasses import dataclass, field
     from typing import List
     
     @dataclass
     class ModelDownloaderApi:
      repo_id: str = ""
      file_queue: List[dict] = field(default_factory=list)
      total_size: int = 0
      fs: HfFileSystem = field(default_factory=HfFileSystem)
      repo_folder: str = ""
     
      def get_info(self, repo_id: str, is_sd: bool = False) -> None:
          self.repo_id = repo_id
          self.repo_folder = repo_id.replace('/', '---')
          self.file_queue.clear()
          self.total_size = 0
          self.enum_file_list(repo_id, is_sd, True)
          print(dumps({"total_size": self.total_size, "file_list": self.file_queue}))

3. Verify with Unit Tests:

   • Run pytest to execute all unit tests and ensure that the refactored code behaves correctly.

Steps to Reproduce:

1. Clone the repository:

   git clone <repository-url>

2. Navigate to the directory containing the code to be refactored.
3. Identify classes that can be converted to dataclasses.
4. Refactor the classes by adding the @dataclass decorator and removing boilerplate code.
5. Run the tests to verify the changes:

   pytest

Expected Behavior:

• The codebase should use dataclasses for key classes, reducing boilerplate code and improving readability.
• All unit tests should pass, confirming that functionality remains unchanged.

Current Behavior:

• The codebase currently uses traditional class definitions, which include boilerplate code for methods such as __init__() and __repr__().

Additional Information:

Progress Log:

• 2024-07-27: Initial setup of the issue and identification of key classes for refactoring.