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* feat: declarative fallback generalizations for unknown models Unknown or newly-released models previously degraded (missed cost lookups, wrong supports_* flags, broken provider routing) and were patched with one-off hardcoded regexes scattered across Python. This adds a single data-driven source of truth: a fallback_generalizations block in model_prices_and_context_window.json holding ordered, case-insensitive regex rules that map a model name to the metadata to apply when it has no exact entry. A new fallback_generalizations module owns the rules and a compiled-regex cache that is built once and invalidated on reload, so the O(n) scan runs only on a cache miss. get_llm_provider now routes an otherwise-unknown model via the first matching rule's litellm_provider, replacing the hardcoded _CLAUDE_PATTERN and _matches_claude_model_pattern. _get_model_info_helper falls back to a matching rule's model_info after the exact lookups miss, so get_model_info and the supports_* helpers resolve unknown models from the same rule. get_model_cost_map extracts the block out of the returned map, and the integrity check now counts real model entries (excluding reserved meta keys) so the new key cannot mask a genuinely shrunk upstream file. The top level of the file stays a flat map of models so existing litellm releases that fetch the live file keep working and keep receiving updates; the block ships in both the root file and the bundled backup. An anthropic-claude rule reproduces the old future-claude routing and additionally supplies capability flags and a context window https://claude.ai/code/session_01G8Jro8dPLktwnaaSJwVDpo * refactor(anthropic): derive adaptive-thinking from a version threshold; harden generalizations Replace the per-minor-version _is_claude_4_6_model / _is_claude_4_7_model substring matchers with a single _claude_version_at_least predicate that parses the Claude family version from the model name and compares against 4.6. This covers 4.8/4.9/5.x without a code change (the old matchers missed 4.8 entirely) while keeping an explicit supports_adaptive_thinking flag authoritative when present, so there is one source of truth. The two direct call sites in the chat transformation now route through _is_adaptive_thinking_model instead of the deleted matchers. Also address review feedback on the generalizations module: return a copy of the matched model_info so a future caller cannot mutate the compiled-rule cache, document that patterns are matched with re.search and must anchor with ^ and $, and reindent the fallback_generalizations block to the file's 2-space style in both JSON files. https://claude.ai/code/session_01G8Jro8dPLktwnaaSJwVDpo * fix(anthropic): surface adaptive-thinking from the cost map; fix date misparse supports_adaptive_thinking shipped in the model cost map but was never declared on ModelInfo nor copied during construction, so get_model_info (and the supports_* factory) silently dropped it for every provider-prefixed or generalized name; only a bare base entry resolved. Wire it through ModelInfo like the other capability flags and backfill the flag onto the genuine Claude 4.6/4.7/4.8 entries across providers so the data, not code, declares the capability. The anthropic-claude fallback rule also carries the flag (and now accepts a dotted minor, e.g. 4.6) so an unmapped future Claude degrades to adaptive thinking without a code change. Tighten the Claude version parser so an eight-digit date suffix (claude-opus-4-20250514, the non-adaptive Opus 4.0) is no longer read as minor 4.20250514. The cost map stays authoritative; the version check is only a fallback for provider-prefixed names (bedrock/invoke routes, -v1-less ids) that resolve to no mapped entry and so cannot be reached by an exact lookup or the bare-name rule. https://claude.ai/code/session_01G8Jro8dPLktwnaaSJwVDpo * fix(anthropic): date-safe adaptive-thinking version fallback, conservative fallback pricing, ruff strict gate Reconcile adaptive-thinking detection after merging litellm_internal_staging. Keep the cost-map resolver (_supports_model_capability) as the source of truth and add a date-safe opus/sonnet/haiku >= 4.6 name version as a fallback for provider-prefixed ids the cost map cannot resolve (e.g. bedrock/invoke/us.anthropic.claude-opus-4-6). A two-digit cap on the minor keeps an eight-digit date suffix from being misread as a minor version, so the dated Claude 4.0 release stays non-adaptive Price the shipped anthropic-claude fallback rule at the Opus tier so an unknown or newly released Claude is over-costed rather than billed as free Drop the module-level global state in fallback_generalizations (PLW0603) in favor of a small registry object, and switch its annotations plus the new utils helper to builtin generics (UP006), bringing the ruff strict-rule totals back under ceiling * refactor(anthropic): drive adaptive-thinking version gate from a declarative rule Replace the bespoke _claude_version_at_least heuristic with a version-gated fallback_generalizations rule. Unmapped Claude ids now resolve adaptive thinking purely from the cost map: an explicit entry, or the new self-contained anthropic-claude-adaptive-thinking rule that matches opus/sonnet/haiku >= 4.6 (covering 5.x, 6.x and beyond with no code change). New families ship via Price Data Reload instead of a code edit The rule carries the same Opus-tier pricing as the broad anthropic-claude rule plus supports_adaptive_thinking, and is matched first; the broad rule stays version-neutral, so an unmapped >= 4.6 Claude resolves to full pricing and the adaptive flag from one rule, while a sub-4.6 alias such as claude-opus-4-0 is still priced yet stays non-adaptive. The regex caps the minor at two digits so a dated 4.0 id (...-4-20250514) is never read as a >= 4.6 minor * refactor(anthropic): dedupe adaptive-thinking rule via declarative extends The version-gated anthropic-claude-adaptive-thinking rule duplicated the broad anthropic-claude rule's entire Opus-tier price block because rules do not merge: first match wins and returns one rule's whole model_info, so the adaptive rule had to be self-contained. Add a declarative extends field to fallback_generalizations: a rule names a parent and inherits its model_info, with its own keys overriding. Inheritance is resolved once at install time against each rule's raw model_info, so the adaptive rule now carries only its delta (supports_adaptive_thinking) and inherits pricing from the broad rule. Runtime matching, provider routing and gating are unchanged; the broad rule stays anchored and first-match-wins still holds. * docs(anthropic): add ignored description key documenting each generalization regex * fix(anthropic): drop fabricated pricing from the anthropic-claude fallback rule Per review feedback, the base rule no longer carries input/output/cache costs, and the adaptive-thinking rule that extends it inherits that no-pricing model_info. Pricing an unmapped model at a guessed tier reports a confidently-wrong cost without the caller knowing; dropping it keeps the standard unpriced behavior (zero, not a fabricated number) so a missing price stays visible. The rules still supply provider routing, context window, and capability flags, so a brand-new Claude can still be called and its capabilities (including adaptive thinking for >= 4.6) resolved. Description and tests updated to match
281 lines
9.9 KiB
Python
281 lines
9.9 KiB
Python
# conftest.py
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#
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# xdist-compatible test isolation for local_testing tests.
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# Pattern matches tests/test_litellm/conftest.py:
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# - Function-scoped fixture saves/restores litellm globals (no reload)
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# - Module-scoped fixture reloads only in single-process mode
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#
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# IMPORTANT: True defaults are captured at conftest import time (before any
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# test module can pollute them via module-level assignments like
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# `litellm.num_retries = 3`). The function-scoped fixture resets globals to
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# these true defaults before every test, preventing cross-test contamination
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# under xdist where module reload is skipped.
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import importlib
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import os
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import sys
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import pytest
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import litellm
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# ``litellm.model_cost`` is loaded at import time from the URL pinned to ``main``
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# (``LITELLM_MODEL_COST_MAP_URL``). The in-tree backup ships with this branch
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# and can include pricing entries that ``main`` has not yet picked up (e.g.
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# Mistral now returns ``ministral-8b-2512`` from ``mistral-tiny`` and the entry
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# was added on this branch). Backfill any entries that are missing from the
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# remote-fetched map so cost-calculator lookups in tests succeed against the
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# cassette state the branch is being tested with.
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from litellm.litellm_core_utils.get_model_cost_map import (
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RESERVED_TOP_LEVEL_KEYS,
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GetModelCostMap,
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)
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for _k, _v in GetModelCostMap.load_local_model_cost_map().items():
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if _k in RESERVED_TOP_LEVEL_KEYS:
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continue
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litellm.model_cost.setdefault(_k, _v)
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from tests._vcr_conftest_common import ( # noqa: E402,F401
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VerboseReporterState,
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_pin_multipart_boundary,
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apply_vcr_auto_marker_to_items,
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emit_cassette_cache_session_banner,
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emit_vcr_classification_summary,
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emit_vcr_diagnostic_log,
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install_live_call_probe,
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record_vcr_outcome,
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register_persister_if_enabled,
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reset_vcr_diag_dir,
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vcr_config_dict,
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)
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from tests.fake_openai_endpoint import ensure_fake_openai_endpoint # noqa: E402
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@pytest.fixture(scope="session", autouse=True)
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def fake_openai_endpoint():
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ensure_fake_openai_endpoint()
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yield
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# Per-item respx detection (``apply_vcr_auto_marker_to_items``) auto-skips
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# tests whose ``@pytest.mark.respx`` marker or ``respx_mock`` fixture
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# would conflict with vcrpy's transport patch. We no longer maintain a
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# file-level ``_RESPX_CONFLICTING_FILES`` list here — the previous
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# entries (``test_router.py``) had only a stale ``from respx import
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# MockRouter`` import with no actual respx wiring, so file-level
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# blacklisting was masking valid cache opportunities.
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# Files where VCR replay breaks the test:
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# - ``test_router_caching.py``: asserts upstream returns a *new* id per call,
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# which a deterministic cassette replay violates.
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_VCR_INCOMPATIBLE_FILES = frozenset(
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{
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"test_router_caching.py",
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# Hits the local fake OpenAI endpoint on 127.0.0.1; nothing to record.
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"test_fake_openai_endpoint.py",
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}
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)
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# Individual tests (vs. whole files above) that VCR replay can't model:
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# - ``test_router_text_completion_client``: a concurrency test that fires 300
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# identical requests to verify the async OpenAI client is *reused* across
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# calls (per its own comment, it "fails when we create a new Async OpenAI
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# client per request"). vcrpy patches the HTTP transport, so replay never
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# opens real connections and cannot exercise the client pool the test exists
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# to validate. Recording instead stores ~300 near-identical episodes, which
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# blows past MAX_EPISODES_PER_CASSETTE (50) so the cassette is refused on
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# every run (MISS:OVERFLOW). The endpoint is a free mock, so the live calls
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# carry no real provider cost.
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_VCR_INCOMPATIBLE_NODEID_SUFFIXES: tuple[str, ...] = (
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"test_router.py::test_router_text_completion_client",
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)
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_verbose_state = VerboseReporterState()
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@pytest.fixture(scope="module")
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def vcr_config():
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return vcr_config_dict()
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def pytest_recording_configure(config, vcr):
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register_persister_if_enabled(vcr)
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@pytest.hookimpl(hookwrapper=True)
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def pytest_runtest_makereport(item, call):
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outcome = yield
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rep = outcome.get_result()
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setattr(item, f"rep_{rep.when}", rep)
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@pytest.fixture(autouse=True)
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def _vcr_outcome_gate(request, vcr):
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install_live_call_probe(request, vcr)
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yield
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record_vcr_outcome(request, vcr)
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def pytest_configure(config):
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_verbose_state.remember_pluginmanager(config)
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reset_vcr_diag_dir()
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def pytest_runtest_logreport(report):
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_verbose_state.maybe_emit_verdict(report)
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def pytest_terminal_summary(terminalreporter, exitstatus, config):
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emit_cassette_cache_session_banner(terminalreporter)
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emit_vcr_classification_summary(terminalreporter)
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emit_vcr_diagnostic_log(terminalreporter)
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# ---------------------------------------------------------------------------
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# Capture TRUE defaults at conftest import time. This runs before any test
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# module's top-level code (e.g. `litellm.num_retries = 3`) executes, so
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# the values here are guaranteed to be the real package defaults.
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# ---------------------------------------------------------------------------
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_SCALAR_DEFAULTS = {
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"num_retries": getattr(litellm, "num_retries", None),
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"num_retries_per_request": getattr(litellm, "num_retries_per_request", None),
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"request_timeout": getattr(litellm, "request_timeout", None),
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"set_verbose": getattr(litellm, "set_verbose", False),
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"cache": getattr(litellm, "cache", None),
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"allowed_fails": getattr(litellm, "allowed_fails", 3),
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"default_fallbacks": getattr(litellm, "default_fallbacks", None),
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"enable_azure_ad_token_refresh": getattr(
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litellm, "enable_azure_ad_token_refresh", None
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),
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"tag_budget_config": getattr(litellm, "tag_budget_config", None),
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"model_cost": getattr(litellm, "model_cost", None),
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"token_counter": getattr(litellm, "token_counter", None),
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"disable_aiohttp_transport": getattr(litellm, "disable_aiohttp_transport", False),
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"force_ipv4": getattr(litellm, "force_ipv4", False),
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"drop_params": getattr(litellm, "drop_params", None),
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"modify_params": getattr(litellm, "modify_params", False),
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"api_base": getattr(litellm, "api_base", None),
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"api_key": getattr(litellm, "api_key", None),
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}
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@pytest.fixture(scope="function", autouse=True)
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def isolate_litellm_state():
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"""
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Per-function isolation fixture.
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Resets litellm globals to their true defaults before each test and
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restores them afterward, so tests don't leak side effects.
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Works safely under pytest-xdist parallel execution.
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"""
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# ---- Save current callback state (for teardown restore) ----
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original_state = {}
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for attr in (
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"callbacks",
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"success_callback",
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"failure_callback",
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"_async_success_callback",
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"_async_failure_callback",
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):
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if hasattr(litellm, attr):
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val = getattr(litellm, attr)
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original_state[attr] = val.copy() if val else []
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# Save list-type globals
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for attr in ("pre_call_rules", "post_call_rules"):
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if hasattr(litellm, attr):
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val = getattr(litellm, attr)
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original_state[attr] = val.copy() if val else []
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# Save scalar globals
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for attr in _SCALAR_DEFAULTS:
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if hasattr(litellm, attr):
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original_state[attr] = getattr(litellm, attr)
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# ---- Reset to true defaults before the test ----
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# Flush HTTP client cache
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if hasattr(litellm, "in_memory_llm_clients_cache"):
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litellm.in_memory_llm_clients_cache.flush_cache()
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# Clear callbacks and rules
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for attr in (
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"callbacks",
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"success_callback",
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"failure_callback",
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"_async_success_callback",
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"_async_failure_callback",
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"pre_call_rules",
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"post_call_rules",
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):
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if hasattr(litellm, attr):
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setattr(litellm, attr, [])
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# Reset scalar globals to true defaults (prevents contamination from
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# module-level code like `litellm.num_retries = 3` in test files)
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for attr, default_val in _SCALAR_DEFAULTS.items():
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if hasattr(litellm, attr):
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setattr(litellm, attr, default_val)
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yield
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# ---- Teardown: restore saved state ----
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if hasattr(litellm, "in_memory_llm_clients_cache"):
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litellm.in_memory_llm_clients_cache.flush_cache()
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for attr, original_value in original_state.items():
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if hasattr(litellm, attr):
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setattr(litellm, attr, original_value)
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@pytest.fixture(scope="module", autouse=True)
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def setup_and_teardown():
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"""
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Module-scoped setup. Reloads litellm only in single-process mode
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(skipped under xdist to avoid cross-worker interference).
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"""
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sys.path.insert(0, os.path.abspath("../.."))
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import litellm
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worker_id = os.environ.get("PYTEST_XDIST_WORKER", None)
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if worker_id is None:
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importlib.reload(litellm)
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try:
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if hasattr(litellm, "proxy") and hasattr(litellm.proxy, "proxy_server"):
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import litellm.proxy.proxy_server
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importlib.reload(litellm.proxy.proxy_server)
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except Exception as e:
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print(f"Error reloading litellm.proxy.proxy_server: {e}")
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if hasattr(litellm, "in_memory_llm_clients_cache"):
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litellm.in_memory_llm_clients_cache.flush_cache()
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yield
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def pytest_collection_modifyitems(config, items):
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apply_vcr_auto_marker_to_items(
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items,
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skip_files=_VCR_INCOMPATIBLE_FILES,
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skip_nodeid_suffixes=_VCR_INCOMPATIBLE_NODEID_SUFFIXES,
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)
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# Separate tests in 'test_amazing_proxy_custom_logger.py' and other tests
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custom_logger_tests = [
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item for item in items if "custom_logger" in item.parent.name
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]
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other_tests = [item for item in items if "custom_logger" not in item.parent.name]
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# Sort tests based on their names
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custom_logger_tests.sort(key=lambda x: x.name)
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other_tests.sort(key=lambda x: x.name)
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# Reorder the items list
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items[:] = custom_logger_tests + other_tests
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