Write tests that are (a) high-signal and not fragile, and (b) structured with standard pytest mechanisms.

Apply:

• Use fixtures instead of module/global clients, so each test has clean, controllable dependencies.
• For new externally observable behavior, add a targeted test. If the output is complex, use a snapshot/recorded example.
• For internal “plumbing” where deep inspection isn’t possible, start with a smoke test (no exceptions) and optionally mock the dependency to assert that key arguments are passed.
• Use pytest.MonkeyPatch.context() (or equivalent) to ensure patches clean up at the right scope.
• Skip conditionally with pytest.mark.skipif(...) instead of early-return branches.
• Assert only the behavior that matters; avoid extra checks that are likely to be stripped/modified by proxies (e.g., don’t add confusing header assertions when you only need to verify Authorization).

Example (focused assertion + fixture-friendly structure):

import re
import pytest
import respx
import httpx
from unittest.mock import Mock

@pytest.mark.respx()
def test_bearer_token_env_passes_authorization(monkeypatch: pytest.MonkeyPatch, respx_mock):
    respx_mock.post(re.compile(r"https://bedrock-runtime\.us-east-1\.amazonaws\.com/model/.*/invoke")).mock(
        return_value=httpx.Response(200, json={"ok": True})
    )

    monkeypatch.setenv("AWS_BEARER_TOKEN_BEDROCK", "test-bearer-token")

    # exercise code that triggers the request
    # sync_client.messages.create(...)

    calls = list(respx_mock.calls)
    assert len(calls) == 1
    assert calls[0].request.headers.get("Authorization") == "Bearer test-bearer-token"

This approach keeps tests reliable, maintainable, and valuable as the code evolves.