Databricks-labs-blueprint

* If `Threads.strict()` raises just one error, don't wrap it with `ManyError` ([79](https://github.com/databrickslabs/blueprint/issues/79)). The `strict` method in the `gather` function of the `parallel.py` module in the `databricks/labs/blueprint` package has been updated to change the way it handles errors. Previously, if any task in the `tasks` sequence failed, the `strict` method would raise a `ManyError` exception containing all the errors. With this change, if only one error occurs, that error will be raised directly without being wrapped in a `ManyError` exception. This simplifies error handling and avoids unnecessary nesting of exceptions. Additionally, the `__tracebackhide__` dunder variable has been added to the method to improve the readability of tracebacks by hiding it from the user. This update aims to provide a more streamlined and user-friendly experience for handling errors in parallel processing tasks.

* Fixed marshalling & unmarshalling edge cases ([76](https://github.com/databrickslabs/blueprint/issues/76)). The serialization and deserialization methods in the code have been updated to improve handling of edge cases during marshalling and unmarshalling of data. When encountering certain edge cases, the `_marshal_list` method will now return an empty list instead of None, and both the `_unmarshal` and `_unmarshal_dict` methods will return None as is if the input is None. Additionally, the `_unmarshal` method has been updated to call `_unmarshal_generic` instead of checking if the type reference is a dictionary or list when it is a generic alias. The `_unmarshal_generic` method has also been updated to handle cases where the input is None. A new test case, `test_load_empty_data_class()`, has been added to the `tests/unit/test_installation.py` file to verify this behavior, ensuring that the correct behavior is maintained when encountering these edge cases during the marshalling and unmarshalling processes. These changes increase the reliability of the serialization and deserialization processes.

* Fixed edge cases when loading typing.Dict, typing.List and typing.ClassVar ([74](https://github.com/databrickslabs/blueprint/issues/74)). In this release, we have implemented changes to improve the handling of edge cases related to the Python `typing.Dict`, `typing.List`, and `typing.ClassVar` during serialization and deserialization of dataclasses and generic types. Specifically, we have modified the `_marshal` and `_unmarshal` functions to check for the `__origin__` attribute to determine whether the type is a `ClassVar` and skip it if it is. The `_marshal_dataclass` and `_unmarshal_dataclass` functions now check for the `__dataclass_fields__` attribute to ensure that only dataclass fields are marshaled and unmarshaled. We have also added a new unit test for loading a complex data class using the `MockInstallation` class, which contains various attributes such as a string, a nested dictionary, a list of `Policy` objects, and a dictionary mapping string keys to `Policy` objects. This test case checks that the installation object correctly serializes and deserializes the `ComplexClass` instance to and from JSON format according to the specified attribute types, including handling of the `typing.Dict`, `typing.List`, and `typing.ClassVar` types. These changes improve the reliability and robustness of our library in handling complex data types defined in the `typing` module.
* `MockPrompts.extend()` now returns a copy ([72](https://github.com/databrickslabs/blueprint/issues/72)). In the latest release, the `extend()` method in the `MockPrompts` class of the `tui.py` module has been enhanced. Previously, `extend()` would modify the original `MockPrompts` object, which could lead to issues when reusing the same object in multiple places within the same test, as its state would be altered each time `extend()` was called. This has been addressed by updating the `extend()` method to return a copy of the `MockPrompts` object with the updated patterns and answers, instead of modifying the original object. This change ensures that the original `MockPrompts` object can be securely reused in multiple test scenarios without unintended side effects, preserving the integrity of the original state. Furthermore, additional tests have been incorporated to verify the correct behavior of both the new and original prompts.

* Fixed `MockInstallation` to emulate workspace-global setup ([69](https://github.com/databrickslabs/blueprint/issues/69)). In this release, the `MockInstallation` class in the `installation` module has been updated to better replicate a workspace-global setup, enhancing testing and development accuracy. The `is_global` method now utilizes the `product` method instead of `_product`, and a new instance variable `_is_global` with a default value of `True` is introduced in the `__init__` method. Moreover, a new `product` method is included, which consistently returns the string "mock". These enhancements resolve issue [#69](https://github.com/databrickslabs/blueprint/issues/69), "Fixed `MockInstallation` to emulate workspace-global setup", ensuring the `MockInstallation` instance behaves as a global installation, facilitating precise and reliable testing and development for our software engineering team.
* Improved `MockPrompts` with `extend()` method ([68](https://github.com/databrickslabs/blueprint/issues/68)). In this release, we've added an `extend()` method to the `MockPrompts` class in our library's TUI module. This new method allows developers to add new patterns and corresponding answers to the existing list of questions and answers in a `MockPrompts` object. The added patterns are compiled as regular expressions and the questions and answers list is sorted by the length of the regular expression patterns in descending order. This feature is particularly useful for writing tests where prompt answers need to be changed, as it enables better control and customization of prompt responses during testing. By extending the list of questions and answers, you can handle additional prompts without modifying the existing ones, resulting in more organized and maintainable test code. If a prompt hasn't been mocked, attempting to ask a question with it will raise a `ValueError` with an appropriate error message.
* Use Hatch v1.9.4 to as build machine requirement ([70](https://github.com/databrickslabs/blueprint/issues/70)). The Hatch package version for the build machine requirement has been updated from 1.7.0 to 1.9.4 in this change. This update streamlines the Hatch setup and version management, removing the specific installation step and listing `hatch` directly in the required field. The pre-setup command now only includes "hatch env create". Additionally, the acceptance tool version has been updated to ensure consistent project building and testing with the specified Hatch version. This change is implemented in the acceptance workflow file and the version of the acceptance tool used by the sandbox. This update ensures that the project can utilize the latest features and bug fixes available in Hatch 1.9.4, improving the reliability and efficiency of the build process. This change is part of the resolution of issue [#70](https://github.com/databrickslabs/blueprint/issues/70).

* Added commands with interactive prompts ([66](https://github.com/databrickslabs/blueprint/issues/66)). This commit introduces a new feature in the Databricks Labs project to support interactive prompts in the command-line interface (CLI) for enhanced user interactivity. The `Prompts` argument, imported from `databricks.labs.blueprint.tui`, is now integrated into the `app.command` decorator, enabling the creation of commands with user interaction like confirmation prompts. An example of this is the `me` command, which confirms whether the user wants to proceed before displaying the current username. The commit also refactored the code to make it more efficient and maintainable, removing redundancy in creating client instances. The `AccountClient` and `WorkspaceClient` instances can now be provided automatically with the product name and version. These changes improve the CLI by making it more interactive, user-friendly, and adaptable to various use cases while also optimizing the codebase for better efficiency and maintainability.
* Added more code documentation ([64](https://github.com/databrickslabs/blueprint/issues/64)). This release introduces new features and updates to various files in the open-source library. The `cli.py` file in the `src/databricks/labs/blueprint` directory has been updated with a new decorator, `command`, which registers a function as a command. The `entrypoint.py` file in the `databricks.labs.blueprint` module now includes a module-level docstring describing its purpose, as well as documentation for the various standard libraries it imports. The `Installation` class in the `installers.py` file has new methods for handling files, such as `load`, `load_or_default`, `upload`, `load_local`, and `files`. The `installers.py` file also includes a new `InstallationState` dataclass, which is used to track installations. The `limiter.py` file now includes code documentation for the `RateLimiter` class and the `rate_limited` decorator, which are used to limit the rate of requests. The `logger.py` file includes a new `NiceFormatter` class, which provides a nicer format for logging messages with colors and bold text if the console supports it. The `parallel.py` file has been updated with new methods for running tasks in parallel and returning results and errors. The `TUI.py` file has been documented, and includes imports for logging, regular expressions, and collections abstract base class. Lastly, the `upgrades.py` file has been updated with additional code documentation and new methods for loading and applying upgrade scripts. Overall, these changes improve the functionality, maintainability, and usability of the open-source library.
* Fixed init-project command ([65](https://github.com/databrickslabs/blueprint/issues/65)). In this release, the `init-project` command has been improved with several bug fixes and new functionalities. A new import statement for the `sys` module has been added, and a `docs` directory is now included in the copied directories and files during initialization. The `init_project` function has been updated to open files using the default system encoding, ensuring proper reading and writing of file contents. The `relative_paths` function in the `entrypoint.py` file now returns absolute paths if the common path is the root directory, addressing issue [#41](https://github.com/databrickslabs/blueprint/issues/41). Additionally, several test functions have been added to `tests/unit/test_entrypoint.py`, enhancing the reliability and robustness of the `init-project` command by providing comprehensive tests for supporting functions. Overall, these changes significantly improve the functionality and reliability of the `init-project` command, ensuring a more consistent and accurate project initialization process.
* Using `ProductInfo` with integration tests ([63](https://github.com/databrickslabs/blueprint/issues/63)). In this update, the `ProductInfo` class has been enhanced with a new class method `for_testing(klass)` to facilitate effective integration testing. This method generates a new `ProductInfo` object with a random `product_name`, enabling the creation of distinct installation directories for each test execution. Prior to this change, conflicts and issues could arise when multiple test executions shared the same integration test folder. With the introduction of this new method, developers can now ensure that their integration tests run with unique product names and separate installation directories, enhancing testing isolation and accuracy. This update is demonstrated in the provided code snippet and includes a new test case to confirm the generation of unique product names. Furthermore, a pre-existing test case has been modified to provide a more specific error message related to the `SingleSourceVersionError`. This enhancement aims to improve the integration testing capabilities of the codebase and is designed to be easily adopted by other software engineers utilizing this project.

* Fixed the order of marshal to handle Dataclass with as_dict before other types to avoid SerdeError ([60](https://github.com/databrickslabs/blueprint/issues/60)). In this release, we have addressed an issue that caused a SerdeError during the installation.save operation with a Dataclass object. The error was due to the order of evaluation in the _marshal_dataclass method. The order has been updated to evaluate the `as_dict` method first if it exists in the Dataclass, which resolves the SerdeError. To ensure the correctness of the fix, we have added a new test_data_class function that tests the save and load functionality with a Dataclass object. The test defines a Policy Dataclass with an `as_dict` method that returns a dictionary representation of the object and checks if the file is written correctly and if the loaded object matches the original object. This change has been thoroughly unit tested to ensure that it works as expected.