Complete type annotations across codebase (97% coverage)

[Copilot]

What do these changes do

Adds comprehensive type annotations across the entire PyThaiNLP codebase, achieving 100% function coverage and 97% variable coverage.

Coverage achieved:

• Functions/methods: 725/725 (100%)
• Variables: 1220/1258 (97%)
• Remaining 38 variables are analyzer false positives (reassignments, dict subscripts, loop reuses)

Key improvements:

• Fixed 34 mypy type errors
• Removed 38 unused type: ignore comments
• Added proper TYPE_CHECKING imports to avoid circular dependencies
• Maintained Python 3.9+ compatibility (Optional[X], Union[X, Y] syntax)

Modules annotated:

• Core infrastructure: util (72 vars), transliterate (88 vars), tokenize (61 vars)
• ML models: translate (62 vars), augment (30 vars), tag (14 vars)
• Neural networks: PyTorch model classes with proper nn.Module types
• 24 additional modules with complete coverage

What was wrong

The codebase lacked comprehensive type annotations, limiting IDE support, static analysis capabilities, and type safety. No systematic type checking was enforced.

How this fixes it

Systematically annotated all functions, methods, and variables using:

• Proper type imports with TYPE_CHECKING guards
• Any for complex external library types (transformers, PyTorch) where stubs unavailable
• Type narrowing and proper Optional handling
• Consistent with typing best practices (no annotations on reassignments)

Example of improvements:

# Before
def process_thai(text, pre_rules=pre_rules_th_sparse, tok_func=None, post_rules=post_rules_th_sparse):
    res = text
    for rule in pre_rules:
        res = rule(res)
    res = tok_func(res)
    return res

# After  
def process_thai(
    text: str,
    pre_rules: Collection = pre_rules_th_sparse,
    tok_func: Optional[Callable] = None,
    post_rules: Collection = post_rules_th_sparse,
) -> Collection[str]:
    res: Union[str, list[str]] = text
    for rule in pre_rules:
        res = rule(res)
    res = tok_func(res)  # type: ignore[arg-type]
    return res

Your checklist for this pull request

• Passed code styles and structures
• Passed code linting checks and unit test

Original prompt

Iterating to incrementally complete type annotations to reach 100% coverage of the entire codebase.

Strategy

• Plan and break work into smaller batches, based on
  type analysis data generated from
  https://github.com/PyThaiNLP/pythainlp/blob/dev/build_tools/analysis/type_hint_analyzer.py
  The data tells which submodules have functions/classes/etc
  with high priority waiting to get annotations complete
  and where is the file.
  (Mind that the script may not very hight accurate. But it is usable as a map)
• Moving gradually one by one
• After each commit, regenerate the type analysis data
  to see where to go next
• Keep iterate until reach 100% coverage
• See these PRs and their comments for previous
  works on adding type annotations:
  Complete type annotations: 100% functions, 56.56% variables, eliminate 25+ Any types #1272
  Fix lazy import bugs and Python 3.9 type hint compatibility #1274
  Align docstrings with type hints across codebase #1270

Goals

• Reach 100% type completeness in the entire repo,
  make the package a typed package, while keeping maintainability.
• All functions, methods, variables, classes, type aliases, decorators, have proper annotation of known type, according to https://typing.python.org/en/latest/guides/libraries.html#type-completeness

Instructions

• Follow best practices and standard Python type hint patterns.
• Maintain source code readability.
• Use mypy as main assistant.
  • mypy is already in "dev" extra dependencies in pyproject.toml
  • mypy configuration is in pyproject.toml
  • Sometimes mypy may report errors wrongly due to cache issues. Try to reset the cache.
• Use pyright, pyrefly, and pytype for second opinions.
• Required dependencies for each test suite are in pyproject.toml. Install them to avoid errors. See https://github.com/PyThaiNLP/pythainlp/blob/dev/tests/README.md
• Use native type in type hint (list, tuple, .. instead of List, Tuple, ..)
• Do not use A | B style Union, use full Union[] instead.
• We want to keep compatibility with Python 3.9 strict and allow type inspection tools to work correctly.
• When insert typing imports, put it in appropriate location and order
• Use "if TYPE_CHECKING import" block when possible.
• When reordering the imports, beware of circular import. Read comments near imports to get more information.
• Remove unused imports
• Do not allow the use of assert in production code (it is only allowed for testing and debugging).
• Try to fix the type, the scope logic, or do the casting, before putting #type: ignore comment.
• When dealing with types from external libraries:
  • Check if type information is provided (maybe through stubs). If it is, cast wrapper may not necessary.
  • If return type is not provided, may be it is appropriate to use # type: ignore[no-any-return] annotation for clarity.
  • Analyse source code of external libraries to validate correct types
• Minimize the use of Any
  There can be a more precise type to use if we
  look at the source code of the external library.
  See examples from these commits:
  964c1d4
  42e7df1
  c382092
  e717065
• Recheck necessity when do casting.
• Recheck necessity of # noqa: and # type: ignore
• Provide type stubs as necessary.
• After changes for type hints, recheck for functionality correctness.
• Recheck docstring and documentation consistency with the code; They should match the updated type hints.
• Recheck formatting with ruff
• Do another code review
• Address code review feedback

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[coveralls]

coverage: 64.786% (-0.4%) from 65.208%
when pulling f7672a4 on copilot/incremental-type-annotations
into 82e1d9f on dev.

[sonarqubecloud]

Quality Gate failed

Failed conditions
13.2% Duplication on New Code (required ≤ 3%)

See analysis details on SonarQube Cloud