# What is this?
## Unit tests for the Scheduler.py (workload prioritization scheduler)

import sys, os, time, openai, uuid
import traceback, asyncio
import pytest
from typing import List

sys.path.insert(
    0, os.path.abspath("../..")
)  # Adds the parent directory to the system path
from litellm import Router
from litellm.scheduler import FlowItem, Scheduler, SchedulerCacheKeys
from litellm import ModelResponse


@pytest.mark.asyncio
async def test_scheduler_diff_model_names():
    """
    Assert 2 requests to 2 diff model groups are top of their respective queue's
    """
    scheduler = Scheduler()

    item1 = FlowItem(priority=0, request_id="10", model_name="gpt-3.5-turbo")
    item2 = FlowItem(priority=0, request_id="11", model_name="gpt-4")
    await scheduler.add_request(item1)
    await scheduler.add_request(item2)

    assert (
        await scheduler.poll(
            id="10", model_name="gpt-3.5-turbo", health_deployments=[{"key": "value"}]
        )
        == True
    )
    assert (
        await scheduler.poll(
            id="11", model_name="gpt-4", health_deployments=[{"key": "value"}]
        )
        == True
    )


@pytest.mark.asyncio
async def test_scheduler_poll_persists_queue_to_cache():
    class StubRedisCache:
        def __init__(self):
            self.store = {}

        async def async_get_cache(self, key, **kwargs):
            return self.store.get(key)

        async def async_set_cache(self, key, value, **kwargs):
            self.store[key] = value

    redis_cache = StubRedisCache()
    scheduler = Scheduler(redis_cache=redis_cache)

    item1 = FlowItem(priority=0, request_id="10", model_name="gpt-3.5-turbo")
    item2 = FlowItem(priority=0, request_id="11", model_name="gpt-3.5-turbo")
    await scheduler.add_request(item1)
    await scheduler.add_request(item2)

    await scheduler.poll(
        id="10", model_name="gpt-3.5-turbo", health_deployments=[]
    )

    queue_key = f"{SchedulerCacheKeys.queue.value}:{item1.model_name}"
    updated_queue = redis_cache.store[queue_key]
    assert updated_queue[0][1] == "11"


@pytest.mark.parametrize("p0, p1", [(0, 0), (0, 1), (1, 0)])
@pytest.mark.parametrize("healthy_deployments", [[{"key": "value"}], []])
@pytest.mark.asyncio
async def test_scheduler_prioritized_requests(p0, p1, healthy_deployments):
    """
    2 requests for same model group
    """
    scheduler = Scheduler()

    item1 = FlowItem(priority=p0, request_id="10", model_name="gpt-3.5-turbo")
    item2 = FlowItem(priority=p1, request_id="11", model_name="gpt-3.5-turbo")
    await scheduler.add_request(item1)
    await scheduler.add_request(item2)

    if p0 == 0:
        assert (
            await scheduler.peek(
                id="10",
                model_name="gpt-3.5-turbo",
                health_deployments=healthy_deployments,
            )
            == True
        ), "queue={}".format(await scheduler.get_queue(model_name="gpt-3.5-turbo"))
        assert (
            await scheduler.peek(
                id="11",
                model_name="gpt-3.5-turbo",
                health_deployments=healthy_deployments,
            )
            == False
        )
    else:
        assert (
            await scheduler.peek(
                id="11",
                model_name="gpt-3.5-turbo",
                health_deployments=healthy_deployments,
            )
            == True
        )
        assert (
            await scheduler.peek(
                id="10",
                model_name="gpt-3.5-turbo",
                health_deployments=healthy_deployments,
            )
            == False
        )


@pytest.mark.asyncio
async def test_scheduler_queue_cleanup_on_timeout():
    """
    Test that a timed-out request is properly removed from the queue.
    This prevents memory leaks from accumulating timed-out requests.
    """
    scheduler = Scheduler()

    # Add multiple requests with different priorities
    item1 = FlowItem(priority=0, request_id="req-0", model_name="gpt-3.5-turbo")
    item2 = FlowItem(priority=1, request_id="req-1", model_name="gpt-3.5-turbo")
    item3 = FlowItem(priority=2, request_id="req-2", model_name="gpt-3.5-turbo")

    await scheduler.add_request(item1)
    await scheduler.add_request(item2)
    await scheduler.add_request(item3)

    # Verify initial queue size
    queue_before = await scheduler.get_queue(model_name="gpt-3.5-turbo")
    assert len(queue_before) == 3, f"Expected 3 items in queue, got {len(queue_before)}"

    # Simulate timeout cleanup - remove a non-front request (item2)
    await scheduler.remove_request(request_id="req-1", model_name="gpt-3.5-turbo")

    # Verify queue was cleaned up
    queue_after = await scheduler.get_queue(model_name="gpt-3.5-turbo")
    assert len(queue_after) == 2, f"Expected 2 items after cleanup, got {len(queue_after)}"

    # Verify the correct request was removed
    remaining_ids = [item[1] for item in queue_after]
    assert "req-1" not in remaining_ids, "Expected req-1 to be removed"
    assert "req-0" in remaining_ids, "Expected req-0 to remain"
    assert "req-2" in remaining_ids, "Expected req-2 to remain"

    # Verify remaining items are in correct priority order (0 should be first)
    assert queue_after[0][1] == "req-0", "Expected req-0 (priority 0) to be at front"