Silence several warnings being emitted by the tests

[dagardner-nv]

Description

• When using AyncMock all attributes return another AyncMock, there were a few cases where a method was being called on an AsyncMock that was expected to be blocking (yielding an ... was never awaited warning). In these situations I explicitly set a blocking mock object.
• Use the model_fields attribute on the class not the instance, avoids Pydantic deprecation warning

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Summary by CodeRabbit

• Refactor

  • Improved component reference discovery by using class-level metadata for more consistent behavior. No API changes or user-facing differences expected.

• Tests

  • Updated tests to align with metadata handling changes.
  • Enhanced HTTP response mocks (including headers and status behavior) for more realistic scenarios.
  • Expanded authentication tests to cover callback setup in token flows.

• Chores

  • General reliability and consistency improvements, with no impact on existing workflows.

[coderabbitai]

Walkthrough

Switches Pydantic field access from instance-level to class-level in recursive component reference discovery; updates tests to use class-level model_fields. Adjusts MCP tests to use MagicMock for raise_for_status, add JSON Content-Type headers in mocked responses, and adds a mocked private callback in token storage tests.

Changes

Cohort / File(s)	Summary of changes
Builder: class-level model_fields access src/nat/builder/component_utils.py	In recursive_componentref_discovery, iterate over type(value).model_fields instead of value.model_fields when value is a BaseModel instance.
Builder tests alignment tests/nat/builder/test_component_utils.py	Update tests to iterate TestConfig.model_fields (class-level) instead of instance_config.model_fields.
Data models tests alignment tests/nat/data_models/test_optimizable.py	Replace dict(m.model_fields)["x"].json_schema_extra usages with dict(M.model_fields)["x"].json_schema_extra (class-level access).
MCP auth provider test mocks tests/nat/mcp/test_mcp_auth_provider.py	Use MagicMock for mock_resp.raise_for_status, and set mock_resp.headers = {"content-type": "application/json"} on relevant mocked HTTP responses.
MCP token storage test mocks tests/nat/mcp/test_mcp_token_storage.py	Add mock_instance._set_custom_auth_callback = MagicMock(...) in the lazy OAuth2 token test setup.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title Check	✅ Passed	The title uses an imperative verb and succinctly describes the primary change of silencing test warnings, aligns with the PR’s objectives, and stays well under the 72-character limit.

✨ Finishing touches

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• Post copyable unit tests in a comment

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📥 Commits

Reviewing files that changed from the base of the PR and between b5170f2 and 6fddb56.

📒 Files selected for processing (1)

• tests/nat/data_models/test_optimizable.py (5 hunks)

🧰 Additional context used

📓 Path-based instructions (5)

**/*.{py,yaml,yml}

📄 CodeRabbit inference engine (.cursor/rules/nat-test-llm.mdc)

**/*.{py,yaml,yml}: Configure response_seq as a list of strings; values cycle per call, and [] yields an empty string.
Configure delay_ms to inject per-call artificial latency in milliseconds for nat_test_llm.

Files:

• tests/nat/data_models/test_optimizable.py

**/*.py

📄 CodeRabbit inference engine (.cursor/rules/nat-test-llm.mdc)

**/*.py: Programmatic use: create TestLLMConfig(response_seq=[...], delay_ms=...), add with builder.add_llm("", cfg).
When retrieving the test LLM wrapper, use builder.get_llm(name, wrapper_type=LLMFrameworkEnum.) and call the framework’s method (e.g., ainvoke, achat, call).

**/*.py: In code comments/identifiers use NAT abbreviations as specified: nat for API namespace/CLI, nvidia-nat for package name, NAT for env var prefixes; do not use these abbreviations in documentation
Follow PEP 20 and PEP 8; run yapf with column_limit=120; use 4-space indentation; end files with a single trailing newline
Run ruff check --fix as linter (not formatter) using pyproject.toml config; fix warnings unless explicitly ignored
Respect naming: snake_case for functions/variables, PascalCase for classes, UPPER_CASE for constants
Treat pyright warnings as errors during development
Exception handling: use bare raise to re-raise; log with logger.error() when re-raising to avoid duplicate stack traces; use logger.exception() when catching without re-raising
Provide Google-style docstrings for every public module, class, function, and CLI command; first line concise and ending with a period; surround code entities with backticks
Validate and sanitize all user input, especially in web or CLI interfaces
Prefer httpx with SSL verification enabled by default and follow OWASP Top-10 recommendations
Use async/await for I/O-bound work; profile CPU-heavy paths with cProfile or mprof before optimizing; cache expensive computations with functools.lru_cache or external cache; leverage NumPy vectorized operations when beneficial

Files:

• tests/nat/data_models/test_optimizable.py

tests/**/*.py

📄 CodeRabbit inference engine (.cursor/rules/general.mdc)

Unit tests reside under tests/ and should use markers defined in pyproject.toml (e.g., integration)

Files:

• tests/nat/data_models/test_optimizable.py

---

⚙️ CodeRabbit configuration file

tests/**/*.py: - Ensure that tests are comprehensive, cover edge cases, and validate the functionality of the code. - Test functions should be named using the test_ prefix, using snake_case. - Any frequently repeated code should be extracted into pytest fixtures. - Pytest fixtures should define the name argument when applying the pytest.fixture decorator. The fixture
function being decorated should be named using the fixture_ prefix, using snake_case. Example:
@pytest.fixture(name="my_fixture")
def fixture_my_fixture():
pass

Files:

• tests/nat/data_models/test_optimizable.py

{tests/**/*.py,examples/*/tests/**/*.py}

📄 CodeRabbit inference engine (.cursor/rules/general.mdc)

{tests/**/*.py,examples/*/tests/**/*.py}: Use pytest (with pytest-asyncio for async); name test files test_*.py; test functions start with test_; extract repeated code into fixtures; fixtures must set name in decorator and be named with fixture_ prefix
Mock external services with pytest_httpserver or unittest.mock; do not hit live endpoints
Mark expensive tests with @pytest.mark.slow or @pytest.mark.integration

Files:

• tests/nat/data_models/test_optimizable.py

**/*

⚙️ CodeRabbit configuration file

**/*: # Code Review Instructions

• Ensure the code follows best practices and coding standards. - For Python code, follow
  PEP 20 and
  PEP 8 for style guidelines.
• Check for security vulnerabilities and potential issues. - Python methods should use type hints for all parameters and return values.
  Example:

  def my_function(param1: int, param2: str) -> bool:
      pass

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  • When re-raising exceptions: use bare raise statements to maintain the original stack trace,
    and use logger.error() (not logger.exception()) to avoid duplicate stack trace output.
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Files:

• tests/nat/data_models/test_optimizable.py

🔇 Additional comments (1)

tests/nat/data_models/test_optimizable.py (1)

74-74: LGTM! Correctly addresses Pydantic deprecation warning.

The switch from instance-level to class-level access of model_fields (e.g., M.model_fields instead of m.model_fields) correctly follows Pydantic v2 best practices and silences the deprecation warning as intended by the PR objectives.

Also applies to: 83-83, 99-99, 119-119, 137-137

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[dagardner-nv]

/merge