"""Generic configuration for live e2e tests against a running LiteLLM proxy.

Shared by every e2e suite under tests/e2e/. Values come from the
environment so the same tests run against localhost or a deployed proxy.
"""

from __future__ import annotations

import os
import time
import uuid
from pathlib import Path

from dotenv import load_dotenv

from fixture_transport import deterministic_marker, parse_fixture_mode

# Local runs keep provider / DataDog keys in tests/e2e/.env (see CONTRIBUTING.md).
# Compose injects them into the proxy container, but pytest on the host does not
# inherit that file unless we load it. override=False so a real shell export wins.
load_dotenv(Path(__file__).resolve().parent / ".env", override=False)

PROXY_BASE_URL = os.environ.get("LITELLM_PROXY_URL", "http://localhost:4000").rstrip("/")
MASTER_KEY = os.environ.get("LITELLM_MASTER_KEY", "sk-1234")

# Control-plane (management/admin) base URL. Defaults to PROXY_BASE_URL so a
# single path-routing host (stage ALB, compose monolith) works for both planes.
# Set LITELLM_CONTROL_PLANE_URL only when management is a different base than
# the LLM host and you are not going through an ingress that path-routes.
CONTROL_PLANE_BASE_URL = os.environ.get(
    "LITELLM_CONTROL_PLANE_URL", PROXY_BASE_URL
).rstrip("/")

UI_USERNAME = os.environ.get("E2E_UI_USERNAME", "admin")
UI_PASSWORD = os.environ.get("E2E_UI_PASSWORD", MASTER_KEY)

# Dashboard base for playwright. Defaults to PROXY_BASE_URL so one ALB/monolith
# host covers /ui as well. Override E2E_UI_BASE_URL only if the UI is elsewhere.
UI_BASE_URL = os.environ.get("E2E_UI_BASE_URL", PROXY_BASE_URL).rstrip("/")

CHEAP_ANTHROPIC_MODEL = os.environ.get("E2E_CHEAP_ANTHROPIC_MODEL", "claude-haiku-4-5")
CHEAP_OPENAI_MODEL = os.environ.get("E2E_CHEAP_OPENAI_MODEL", "gpt-5.5")

LINEAR_MCP_URL = os.environ.get("E2E_LINEAR_MCP_URL", "https://mcp.linear.app/mcp")
LINEAR_STORAGE_STATE = os.environ.get("E2E_LINEAR_STORAGE_STATE", "")

# Jaeger query API of the compose stack's OTEL trace destination (the `jaeger`
# service in docker-compose.yml maps it to host 16686). Trace-completeness tests
# read exported spans back through it.
OTEL_QUERY_URL = os.environ.get("E2E_OTEL_QUERY_URL", "http://localhost:16686").rstrip("/")

# Real-DataDog read-back (no local sink - destination fakes cannot be deployed
# on the cluster): the proxy delivers with DD_API_KEY as in production, and the
# tests read ingested events back through the DataDog Logs Search API, which
# additionally needs an application key. On the cluster the secret manager
# injects both; locally tests/e2e/.env provides them.
DD_SITE = os.environ.get("DD_SITE", "datadoghq.com").strip()
DD_API_KEY = os.environ.get("DD_API_KEY", "").strip()
DD_APP_KEY = os.environ.get("DD_APP_KEY", "").strip()

# After the first event is searchable, keep watching this long for a late
# duplicate before the exactly-one assertion: real-DataDog ingestion jitter can
# make one call's two events searchable tens of seconds apart, and a duplicate
# that surfaces late IS the bug (LIT-4447), so one poll interval is not enough.
DD_SETTLE_SECONDS = float(os.environ.get("E2E_DD_SETTLE_SECONDS", "30"))
# DataDog Logs Search `from` window (relative to now). Wide enough for a suite
# run plus ingestion lag; override if a long CI queue needs a wider lookback.
DD_SEARCH_FROM = os.environ.get("E2E_DD_SEARCH_FROM", "now-30m").strip() or "now-30m"
# The Logs Search API budget is tight - 2 requests per 10s org-wide
# (x-ratelimit-name logs_public_search_api) - so read-backs pace their search
# calls at this interval instead of POLL_INTERVAL, and back off when a 429
# still slips through (the budget is shared with anything else searching).
DD_SEARCH_INTERVAL = float(os.environ.get("E2E_DD_SEARCH_INTERVAL", "10"))

# Writes on the proxy are eventually consistent (e.g. spend rows flush on
# proxy_batch_write_at, ~60s). Read-backs poll to this deadline, never sleep-once.
POLL_TIMEOUT = float(os.environ.get("E2E_POLL_TIMEOUT", "120"))
POLL_INTERVAL = float(os.environ.get("E2E_POLL_INTERVAL", "5"))
REQUEST_TIMEOUT = float(os.environ.get("E2E_REQUEST_TIMEOUT", "60"))

# How long a control-plane write (/model/new, /guardrails, /v1/agents) may take to
# reach EVERY replica. Distinct from POLL_TIMEOUT, which is sized for spend-row
# flush; this one is sized for the proxy's config reload
# (`proxy_config_reload_interval_seconds`, 30s by default and 7s on the e2e stack)
# plus margin.
#
# The barriers below wait this out instead of returning on first sight, because a
# single successful read only proves ONE replica converged: every request opens a
# fresh connection, so a load-balanced Service routes each one independently and
# the next call re-rolls. See ProxyClient._await_model_servable.
PROPAGATION_TIMEOUT = float(os.environ.get("E2E_PROPAGATION_TIMEOUT", "15"))

EXPECT_RUST = os.environ.get("E2E_EXPECT_RUST", "").strip().lower() in ("1", "true", "yes")

# Record/replay fixture selection (see fixture_transport.py). The raw mode value
# is parsed and validated there; "live" (the default, also for empty values)
# means the harness behaves exactly as before this knob existed.
FIXTURE_MODE_RAW = os.environ.get("E2E_FIXTURE_MODE", "live")
FIXTURE_DIR = Path(
    os.environ.get("E2E_FIXTURE_DIR", "").strip()
    or str(Path(__file__).resolve().parent / ".fixtures")
)

# Deliberately modest concurrency. The suite shares its proxy with every other
# suite in the run, and 750 users at spawn rate 50 saturated the request path hard
# enough to distort latency-sensitive neighbours (and to spend real provider money
# fast).
LOAD_USERS = int(os.environ.get("E2E_LOAD_USERS", "200"))
LOAD_SPAWN_RATE = float(os.environ.get("E2E_LOAD_SPAWN_RATE", "20"))
LOAD_DURATION_SECONDS = float(os.environ.get("E2E_LOAD_DURATION_SECONDS", "60"))
LOAD_MAX_FAILURE_RATIO = float(os.environ.get("E2E_LOAD_MAX_FAILURE_RATIO", "0.01"))

# The throughput floor is derived per replica instead of being an absolute fleet
# number, so the verdict does not depend on how many replicas happen to be warm.
# One closed-loop user only ever occupies one replica at a time, so a short serial
# pass measures a single replica's request path: its throughput is 1/latency, and
# the concurrent phase then has to reach at least that much no matter how large
# the fleet is. An absolute floor instead asserted replicas x per-replica rate,
# which reactive autoscaling decides rather than the request path.
LOAD_BASELINE_SECONDS = float(os.environ.get("E2E_LOAD_BASELINE_SECONDS", "15"))
LOAD_MAX_SERIAL_LATENCY_SECONDS = float(os.environ.get("E2E_LOAD_MAX_SERIAL_LATENCY_SECONDS", "0.5"))
LOAD_MIN_CONCURRENCY_EFFICIENCY = float(os.environ.get("E2E_LOAD_MIN_CONCURRENCY_EFFICIENCY", "0.8"))

WEEKLY_ANOMALY_OPT_IN_ENV = "E2E_WEEKLY_ANOMALY"
ANOMALY_SESSIONS = int(os.environ.get("E2E_ANOMALY_SESSIONS", "6"))
ANOMALY_TURNS_PER_SESSION = int(os.environ.get("E2E_ANOMALY_TURNS_PER_SESSION", "6"))
ANOMALY_TURN_ATTEMPTS = int(os.environ.get("E2E_ANOMALY_TURN_ATTEMPTS", "3"))
ANOMALY_MAX_ERROR_RATIO = float(os.environ.get("E2E_ANOMALY_MAX_ERROR_RATIO", "0.05"))
ANOMALY_MIN_WARM_CACHE_READ_SHARE = float(
    os.environ.get("E2E_ANOMALY_MIN_WARM_CACHE_READ_SHARE", "0.65")
)
ANOMALY_MAX_P95_TURN_SECONDS = float(
    os.environ.get("E2E_ANOMALY_MAX_P95_TURN_SECONDS", "30")
)
ANOMALY_MAX_KEY_SPEND_USD = float(
    os.environ.get("E2E_ANOMALY_MAX_KEY_SPEND_USD", "0.60")
)
ANOMALY_SPEND_SETTLE_SECONDS = float(
    os.environ.get("E2E_ANOMALY_SPEND_SETTLE_SECONDS", "75")
)


def datadog_mcp_url(*, toolsets: str = "core") -> str:
    """Regional Datadog remote MCP endpoint for this process's DD_SITE.

    US1 is mcp.datadoghq.com; every other site is mcp.<site> (e.g. us5 ->
    mcp.us5.datadoghq.com). A fixed mcp.datadoghq.com URL 403s when the keys
    belong to a non-US1 org.
    """
    site = (
        os.environ.get("DD_SITE", DD_SITE) or "datadoghq.com"
    ).strip().removeprefix("https://").removeprefix("http://").rstrip("/")
    if site.startswith("app."):
        site = site[len("app.") :]
    host = "mcp.datadoghq.com" if site in ("", "datadoghq.com") else f"mcp.{site}"
    base = f"https://{host}/v1/mcp"
    return f"{base}?toolsets={toolsets}" if toolsets else base


def unique_marker() -> str:
    """A short unique token per call/run, so concurrent runs and the shared
    response cache never collide on prompts, tags, or customer ids. In record
    and replay modes the token is deterministic per test instead, so a replay
    run regenerates the exact requests the record run sent."""
    if parse_fixture_mode(FIXTURE_MODE_RAW) in ("record", "replay"):
        return deterministic_marker()
    return uuid.uuid4().hex[:12]


def settle_propagation(written_at: float) -> None:
    """Block until PROPAGATION_TIMEOUT has elapsed since `written_at`, a
    `time.monotonic()` stamp taken the moment a control-plane write returned.

    Callers that already polled for the object still need this: the poll proves one
    replica has it, not all of them. Waiting out the config-reload budget is what
    makes the object safe to use on whichever replica the next request lands on.
    """
    remaining = PROPAGATION_TIMEOUT - (time.monotonic() - written_at)
    if remaining > 0:
        time.sleep(remaining)