# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional
from unittest.mock import Mock

import os
import pytest
import torch

from vllm.config import (CacheConfig, KVTransferConfig, ModelConfig,
                         SchedulerConfig, SpeculativeConfig, VllmConfig)
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
from vllm.sampling_params import GuidedDecodingParams, SamplingParams
from vllm.v1.core.sched.output import CachedRequestData, SchedulerOutput
from vllm.v1.core.sched.scheduler import Scheduler
from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
                                        KVCacheGroupSpec)
from vllm.v1.outputs import ModelRunnerOutput
from vllm.v1.request import Request, RequestStatus
from vllm.v1.structured_output import StructuredOutputManager

from vllm.v1.structured_output.request import StructuredOutputRequest
from vllm.platforms import current_platform
from ...utils import models_path_prefix


EOS_TOKEN_ID = 50256


def create_scheduler(
    model: str = os.path.join(models_path_prefix, "facebook/opt-125m"),
    max_num_seqs: int = 16,
    max_num_batched_tokens: int = 8192,
    enable_prefix_caching: Optional[bool] = None,
    long_prefill_token_threshold: int = 0,
    disable_chunked_mm_input: bool = False,
    use_kv_connector: bool = False,
    num_blocks: int = 10000,
    block_size: int = 16,
    max_model_len: Optional[int] = None,
    num_speculative_tokens: Optional[int] = None,
    skip_tokenizer_init: bool = False,
) -> Scheduler:
    '''Create scheduler under test.

    Args:
      model: model under test
      max_num_seqs: max sequences to schedule
      max_num_batch_tokens: max num tokens to batch
      enable_prefix_caching: optionally force APC config
                             (True/False) or use default
                             (None)

    Returns:
      {class}`Scheduler` instance
    '''
    if max_model_len is None:
        max_model_len = max_num_batched_tokens
    scheduler_config = SchedulerConfig(
        max_num_seqs=max_num_seqs,
        max_num_batched_tokens=max_num_batched_tokens,
        max_model_len=max_model_len,
        long_prefill_token_threshold=long_prefill_token_threshold,
        disable_chunked_mm_input=disable_chunked_mm_input,
        enable_chunked_prefill=True,
    )
    model_config = ModelConfig(
        model=model,
        task="auto",
        tokenizer=model,
        tokenizer_mode="auto",
        trust_remote_code=True,
        dtype="float16",
        seed=42,
        skip_tokenizer_init=skip_tokenizer_init,
    )
    # Cache config, optionally force APC
    kwargs_cache = ({} if enable_prefix_caching is None else {
        'enable_prefix_caching': enable_prefix_caching
    })
    cache_config = CacheConfig(
        block_size=block_size,
        gpu_memory_utilization=0.9,
        swap_space=0,
        cache_dtype="auto",
        **kwargs_cache,
    )
    kv_transfer_config = KVTransferConfig(
        kv_connector="SharedStorageConnector",
        kv_role="kv_both",
        kv_connector_extra_config={"shared_storage_path": "local_storage"},
    ) if use_kv_connector else None

    speculative_config: Optional[SpeculativeConfig] = None
    if num_speculative_tokens is not None:
        speculative_config = SpeculativeConfig(
            model="ngram", num_speculative_tokens=num_speculative_tokens)

    vllm_config = VllmConfig(
        scheduler_config=scheduler_config,
        model_config=model_config,
        cache_config=cache_config,
        kv_transfer_config=kv_transfer_config,
        speculative_config=speculative_config,
    )
    kv_cache_config = KVCacheConfig(
        num_blocks=num_blocks,  # A large number of blocks to hold all requests
        kv_cache_tensors=[],
        kv_cache_groups=[
            KVCacheGroupSpec(['layer'],
                             FullAttentionSpec(block_size, 1, 1, torch.float32,
                                               False))
        ],
    )
    cache_config.num_gpu_blocks = num_blocks
    return Scheduler(
        vllm_config=vllm_config,
        kv_cache_config=kv_cache_config,
        log_stats=True,
        structured_output_manager=StructuredOutputManager(vllm_config),
    )


def create_requests(num_requests: int,
                    num_tokens: int = 10,
                    mm_positions: Optional[list[PlaceholderRange]] = None,
                    max_tokens: int = 16,
                    stop_token_ids: Optional[list[int]] = None,
                    prompt_logprobs: Optional[int] = None):
    sampling_params = SamplingParams(ignore_eos=False,
                                     max_tokens=max_tokens,
                                     stop_token_ids=stop_token_ids,
                                     prompt_logprobs=prompt_logprobs)
    requests = []
    for i in range(num_requests):
        if mm_positions is not None:
            mm_position = mm_positions[i]
            mm_inputs = [MultiModalKwargs({})] * len(mm_position)
        else:
            mm_position = None
            mm_inputs = None
        request = Request(
            request_id=f"{i}",
            prompt_token_ids=[i] * num_tokens,
            sampling_params=sampling_params,
            pooling_params=None,
            multi_modal_inputs=mm_inputs,
            multi_modal_placeholders=mm_position,
            multi_modal_hashes=None,
            eos_token_id=EOS_TOKEN_ID,
        )
        requests.append(request)
    return requests


def test_add_requests():
    scheduler = create_scheduler()
    requests = create_requests(num_requests=10)

    for i, request in enumerate(requests):
        scheduler.add_request(request)
        assert request.request_id in scheduler.requests
        assert len(scheduler.waiting) == i + 1


def test_finish_request():
    scheduler = create_scheduler()
    requests = create_requests(num_requests=10)
    for request in requests:
        scheduler.add_request(request)

    for i, request in enumerate(requests):
        scheduler.finish_requests(request.request_id,
                                  RequestStatus.FINISHED_ABORTED)
        assert request.request_id not in scheduler.requests
        assert len(scheduler.waiting) == 9 - i


def test_get_num_unfinished_requests():
    scheduler = create_scheduler()
    requests = create_requests(num_requests=10)
    for request in requests:
        scheduler.add_request(request)

    for i, request in enumerate(requests):
        scheduler.finish_requests(request.request_id,
                                  RequestStatus.FINISHED_STOPPED)
        assert scheduler.get_num_unfinished_requests() == len(requests) - i - 1


@pytest.mark.parametrize("enable_prefix_caching, prompt_logprobs", [
    (None, None),
    (True, 5),
])
def test_schedule(enable_prefix_caching: Optional[bool],
                  prompt_logprobs: Optional[int]):
    '''Test scheduling.
    Two cases: default APC/no prompt logprobs; APC=True + prompt logprobs
    '''
    scheduler = create_scheduler(enable_prefix_caching=enable_prefix_caching)
    requests = create_requests(num_requests=10,
                               prompt_logprobs=prompt_logprobs)
    for request in requests:
        scheduler.add_request(request)

    # Test initial scheduling
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == len(requests)
    assert output.scheduled_cached_reqs.num_reqs == 0
    assert len(output.finished_req_ids) == 0
    # Verify all requests are scheduled.
    for req_id, num_tokens in output.num_scheduled_tokens.items():
        assert num_tokens == len(requests[int(req_id)].prompt_token_ids)

    # Verify requests moved from waiting to running
    assert len(scheduler.waiting) == 0
    assert len(scheduler.running) == len(requests)
    for i, request in enumerate(requests):
        assert scheduler.running[i] == request


def test_schedule_multimodal_requests():
    scheduler = create_scheduler(model=os.path.join(models_path_prefix, "llava-hf/llava-1.5-7b-hf"))
    mm_positions = [[PlaceholderRange(offset=i, length=100)]
                    for i in range(10)]
    requests = create_requests(
        num_requests=10,
        num_tokens=200,
        mm_positions=mm_positions,
    )
    for request in requests:
        scheduler.add_request(request)

    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == len(requests)
    assert output.scheduled_cached_reqs.num_reqs == 0
    assert len(output.finished_req_ids) == 0
    for req_id, num_tokens in output.num_scheduled_tokens.items():
        assert num_tokens == len(requests[int(req_id)].prompt_token_ids)
    assert len(output.scheduled_encoder_inputs) == 10
    for req_id, encoder_input in output.scheduled_encoder_inputs.items():
        assert len(encoder_input) == 1


def test_schedule_partial_requests():
    """Test scheduling behavior with partial requests.

    This test verifies that:
    1. The scheduler can handle multiple partial requests in a single step when
       constrained by encoder budget.
    2. A request in RUNNING state may be unscheduled in subsequent steps if
       there is insufficient encoder budget.
    """
    scheduler = create_scheduler(
        model=os.path.join(models_path_prefix, "llava-hf/llava-1.5-7b-hf"),
        max_num_batched_tokens=1024,
    )
    mm_positions = [[PlaceholderRange(offset=100, length=600)]
                    for _ in range(3)]
    requests = create_requests(
        num_requests=3,
        num_tokens=800,
        mm_positions=mm_positions,
    )
    for request in requests:
        scheduler.add_request(request)

    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 3
    assert output.scheduled_cached_reqs.num_reqs == 0
    assert len(output.finished_req_ids) == 0

    assert scheduler.max_num_encoder_input_tokens == 1024
    # The first request is scheduled fully.
    assert output.num_scheduled_tokens[requests[0].request_id] == 800
    # The second request is scheduled partially.
    # The <img> tokens are not scheduled because of the encoder budget.
    assert output.num_scheduled_tokens[requests[1].request_id] == 100
    # The third request is also scheduled partially.
    # The <img> tokens are not scheduled because of the encoder budget.
    assert output.num_scheduled_tokens[requests[2].request_id] == 100
    req_to_index = {
        request.request_id: i
        for i, request in enumerate(requests)
    }
    model_runner_output = ModelRunnerOutput(
        req_ids=[request.request_id for request in requests],
        req_id_to_index=req_to_index,
        # Only the first request has a sampled token id because
        # the rest requests are still being prefilled.
        sampled_token_ids=[[0], [], []],
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    scheduler.update_from_output(output, model_runner_output)

    # Schedule the next step.
    # Only the first and second requests are scheduled.
    # The third request is in the RUNNING state but not scheduled in this step
    # because of the encoder budget.
    output = scheduler.schedule()
    assert len(scheduler.running) == 3
    assert len(output.scheduled_new_reqs) == 0
    assert output.scheduled_cached_reqs.num_reqs == 2
    assert len(output.finished_req_ids) == 0
    assert output.num_scheduled_tokens[requests[0].request_id] == 1
    assert output.num_scheduled_tokens[requests[1].request_id] == 700
    assert requests[2].request_id not in output.num_scheduled_tokens


def test_no_mm_input_chunking():
    # Disable multimodal input chunking.
    scheduler = create_scheduler(
        model=os.path.join(models_path_prefix, "llava-hf/llava-1.5-7b-hf"),
        max_num_batched_tokens=1024,
        disable_chunked_mm_input=True,
        max_model_len=2048,
    )
    mm_positions = [[PlaceholderRange(offset=400, length=800)]]
    requests = create_requests(num_requests=1,
                               num_tokens=1200,
                               mm_positions=mm_positions)
    for request in requests:
        scheduler.add_request(request)

    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 1
    assert output.scheduled_cached_reqs.num_reqs == 0
    assert len(output.finished_req_ids) == 0
    # We want to only see the 400 text tokens at the start scheduled
    assert output.num_scheduled_tokens[requests[0].request_id] == 400

    req_to_index = {
        request.request_id: i
        for i, request in enumerate(requests)
    }
    model_runner_output = ModelRunnerOutput(
        req_ids=[request.request_id for request in requests],
        req_id_to_index=req_to_index,
        sampled_token_ids=[[] for _ in range(len(requests))],
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    scheduler.update_from_output(output, model_runner_output)

    output = scheduler.schedule()
    assert len(scheduler.running) == 1
    assert len(output.scheduled_new_reqs) == 0
    assert output.scheduled_cached_reqs.num_reqs == 1
    assert len(output.finished_req_ids) == 0
    assert output.num_scheduled_tokens[requests[0].request_id] == 800

    # Test that we fail if we disable chunked mm input and use too small
    # of a max_num_batched_tokens for the mm input.
    with pytest.raises(ValueError):
        _ = create_scheduler(
            model=os.path.join(models_path_prefix, "llava-hf/llava-1.5-7b-hf"),
            max_num_batched_tokens=100,
            disable_chunked_mm_input=True,
        )


@pytest.mark.parametrize("enable_prefix_caching", [True, False])
def test_schedule_concurrent_partial_requests(enable_prefix_caching: bool):
    """Test scheduling behavior with concurrent partial requests.

    This test verifies that: there are multiple long prefill requests in the
    RUNNING state, and we can schedule them together.

    """
    scheduler = create_scheduler(
        model=os.path.join(models_path_prefix, "facebook/opt-125m"),
        max_num_batched_tokens=1024,
        long_prefill_token_threshold=400,
        enable_prefix_caching=enable_prefix_caching,
    )
    requests = create_requests(
        num_requests=3,
        num_tokens=800,
    )
    for request in requests:
        scheduler.add_request(request)

    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 3
    assert output.scheduled_cached_reqs.num_reqs == 0
    assert len(output.finished_req_ids) == 0

    # The first request is scheduled partially - 400.
    assert output.num_scheduled_tokens[requests[0].request_id] == 400
    # The second request is scheduled partially - 400.
    assert output.num_scheduled_tokens[requests[1].request_id] == 400
    # The third request is also scheduled partially - 1024 - 400 - 400 = 224.
    assert output.num_scheduled_tokens[requests[2].request_id] == 224
    req_to_index = {
        request.request_id: i
        for i, request in enumerate(requests)
    }
    model_runner_output = ModelRunnerOutput(
        req_ids=[request.request_id for request in requests],
        req_id_to_index=req_to_index,
        sampled_token_ids=[[] for _ in range(len(requests))],
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    scheduler.update_from_output(output, model_runner_output)

    # Schedule the next step. All three requests are running.
    # Processed the remaining prefills of the first and second requests.
    output1 = scheduler.schedule()
    assert len(scheduler.running) == 3
    assert len(output1.scheduled_new_reqs) == 0
    assert output1.scheduled_cached_reqs.num_reqs == 3
    assert len(output1.finished_req_ids) == 0
    assert output1.num_scheduled_tokens[requests[0].request_id] == 400
    assert output1.num_scheduled_tokens[requests[1].request_id] == 400
    assert output1.num_scheduled_tokens[requests[2].request_id] == 224

    # Schedule the third step. All three requests are running.
    # First and second requests are in the decode stage.
    # All the remaining tokens in the third request are processed.
    model_runner_output = ModelRunnerOutput(
        req_ids=[request.request_id for request in requests],
        req_id_to_index=req_to_index,
        sampled_token_ids=[[0], [0]] + [[] for _ in range(len(requests) - 2)],
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    scheduler.update_from_output(output1, model_runner_output)
    output2 = scheduler.schedule()
    assert len(scheduler.running) == 3
    assert len(output2.scheduled_new_reqs) == 0
    assert output2.scheduled_cached_reqs.num_reqs == 3
    assert len(output2.finished_req_ids) == 0
    assert output2.num_scheduled_tokens[requests[0].request_id] == 1
    assert output2.num_scheduled_tokens[requests[1].request_id] == 1
    assert output2.num_scheduled_tokens[
        requests[2].request_id] == 800 - 224 - 224


# def test_stop_via_update_from_output():
#     """Test stopping behavior through update_from_output"""
#     scheduler = create_scheduler(num_speculative_tokens=1)

#     # Test case 1: Stop on EOS token
#     requests = create_requests(num_requests=2, max_tokens=10)
#     for req in requests:
#         req.num_computed_tokens = req.num_tokens
#         scheduler.requests[req.request_id] = req
#         scheduler.running.append(req)

#     scheduler_output = SchedulerOutput(
#         scheduled_new_reqs=[],
#         scheduled_cached_reqs=CachedRequestData.make_empty(),
#         num_scheduled_tokens={
#             requests[0].request_id: 1,
#             requests[1].request_id: 2
#         },
#         total_num_scheduled_tokens=3,
#         scheduled_encoder_inputs={},
#         scheduled_spec_decode_tokens={
#             requests[0].request_id: [],
#             requests[1].request_id: [10]
#         },
#         num_common_prefix_blocks=0,
#         finished_req_ids=set(),
#         free_encoder_input_ids=[],
#         structured_output_request_ids={},
#         grammar_bitmask=None)

#     model_output = ModelRunnerOutput(
#         req_ids=[req.request_id for req in requests],
#         req_id_to_index={
#             req.request_id: i
#             for i, req in enumerate(requests)
#         },
#         sampled_token_ids=[[EOS_TOKEN_ID],
#                            [10,
#                             11]],  # First request hits EOS, second continues
#         spec_token_ids=None,
#         logprobs=None,
#         prompt_logprobs_dict={},
#         pooler_output=[])

#     scheduler.update_from_output(scheduler_output, model_output)

#     # Verify first request stopped, second continues
#     assert len(scheduler.running) == 1
#     assert scheduler.running[0].request_id == requests[1].request_id
#     assert requests[0].status == RequestStatus.FINISHED_STOPPED
#     assert requests[0].request_id in scheduler.finished_req_ids
#     assert list(requests[0].output_token_ids) == [EOS_TOKEN_ID]
#     assert list(requests[1].output_token_ids) == [10, 11]

#     # Test case 2: Stop on custom stop token
#     scheduler = create_scheduler(num_speculative_tokens=2)
#     requests = create_requests(num_requests=2,
#                                max_tokens=10,
#                                stop_token_ids=[42, 43])
#     for req in requests:
#         req.num_computed_tokens = req.num_tokens
#         scheduler.requests[req.request_id] = req
#         scheduler.running.append(req)

#     scheduler_output = SchedulerOutput(
#         scheduled_new_reqs=[],
#         scheduled_cached_reqs=CachedRequestData.make_empty(),
#         num_scheduled_tokens={
#             requests[0].request_id: 3,
#             requests[1].request_id: 2
#         },
#         total_num_scheduled_tokens=5,
#         scheduled_encoder_inputs={},
#         scheduled_spec_decode_tokens={
#             requests[0].request_id: [10, 42],
#             requests[1].request_id: [13]
#         },
#         num_common_prefix_blocks=0,
#         finished_req_ids=set(),
#         free_encoder_input_ids=[],
#         structured_output_request_ids={},
#         grammar_bitmask=None,
#     )

#     model_output = ModelRunnerOutput(
#         req_ids=[req.request_id for req in requests],
#         req_id_to_index={
#             req.request_id: i
#             for i, req in enumerate(requests)
#         },
#         sampled_token_ids=[[10, 42, 12],
#                            [13, 14]],  # First request hits stop token
#         spec_token_ids=None,
#         logprobs=None,
#         prompt_logprobs_dict={},
#         pooler_output=[])

#     scheduler.update_from_output(scheduler_output, model_output)

#     # Verify first request stopped on custom token
#     assert len(scheduler.running) == 1
#     assert scheduler.running[0].request_id == requests[1].request_id
#     assert requests[0].status == RequestStatus.FINISHED_STOPPED
#     assert requests[0].stop_reason == 42
#     assert requests[0].request_id in scheduler.finished_req_ids
#     assert list(requests[0].output_token_ids) == [10, 42]
#     assert list(requests[1].output_token_ids) == [13, 14]

#     # Test case 3: Stop on max tokens
#     scheduler = create_scheduler(num_speculative_tokens=2)
#     requests = create_requests(num_requests=2, max_tokens=2)
#     for req in requests:
#         req.num_computed_tokens = req.num_tokens
#         scheduler.requests[req.request_id] = req
#         scheduler.running.append(req)

#     scheduler_output = SchedulerOutput(
#         scheduled_new_reqs=[],
#         scheduled_cached_reqs=CachedRequestData.make_empty(),
#         num_scheduled_tokens={
#             requests[0].request_id: 3,
#             requests[1].request_id: 1
#         },
#         total_num_scheduled_tokens=4,
#         scheduled_encoder_inputs={},
#         scheduled_spec_decode_tokens={
#             requests[0].request_id: [10, 11],
#             requests[1].request_id: []
#         },
#         num_common_prefix_blocks=0,
#         finished_req_ids=set(),
#         free_encoder_input_ids=[],
#         structured_output_request_ids={},
#         grammar_bitmask=None,
#     )

#     model_output = ModelRunnerOutput(
#         req_ids=[req.request_id for req in requests],
#         req_id_to_index={
#             req.request_id: i
#             for i, req in enumerate(requests)
#         },
#         sampled_token_ids=[[10, 11, 12],
#                            [13]],  # First request exceeds max_tokens
#         spec_token_ids=None,
#         logprobs=None,
#         prompt_logprobs_dict={},
#         pooler_output=[])

#     scheduler.update_from_output(scheduler_output, model_output)

#     # Verify first request stopped due to length
#     assert len(scheduler.running) == 1
#     assert scheduler.running[0].request_id == requests[1].request_id
#     assert requests[0].status == RequestStatus.FINISHED_LENGTH_CAPPED
#     assert requests[0].request_id in scheduler.finished_req_ids
#     assert list(requests[0].output_token_ids) == [10, 11
#                                                   ]  # Truncated to max_tokens
#     assert list(requests[1].output_token_ids) == [13]

#     # Test case 4: Ignore EOS flag
#     scheduler = create_scheduler(num_speculative_tokens=2)
#     requests = create_requests(num_requests=1, max_tokens=10)
#     requests[0].sampling_params.ignore_eos = True
#     requests[0].num_computed_tokens = requests[0].num_tokens
#     scheduler.requests[requests[0].request_id] = requests[0]
#     scheduler.running.append(requests[0])

#     scheduler_output = SchedulerOutput(
#         scheduled_new_reqs=[],
#         scheduled_cached_reqs=CachedRequestData.make_empty(),
#         num_scheduled_tokens={requests[0].request_id: 3},
#         total_num_scheduled_tokens=3,
#         scheduled_encoder_inputs={},
#         scheduled_spec_decode_tokens={
#             requests[0].request_id: [EOS_TOKEN_ID, 10]
#         },
#         num_common_prefix_blocks=0,
#         finished_req_ids=set(),
#         free_encoder_input_ids=[],
#         structured_output_request_ids={},
#         grammar_bitmask=None)

#     model_output = ModelRunnerOutput(
#         req_ids=[requests[0].request_id],
#         req_id_to_index={requests[0].request_id: 0},
#         sampled_token_ids=[[EOS_TOKEN_ID, 10, 11]],
#         spec_token_ids=None,
#         logprobs=None,
#         prompt_logprobs_dict={},
#         pooler_output=[])

#     scheduler.update_from_output(scheduler_output, model_output)

#     # Verify request continues past EOS
#     assert len(scheduler.running) == 1
#     assert not requests[0].is_finished()
#     assert list(requests[0].output_token_ids) == [EOS_TOKEN_ID, 10, 11]


@pytest.mark.parametrize("enable_prefix_caching, prompt_logprobs", [
    (None, None),
    (True, 5),
])
def test_schedule_concurrent_batches(enable_prefix_caching: Optional[bool],
                                     prompt_logprobs: Optional[int]):
    scheduler = create_scheduler(
        max_num_batched_tokens=1024,
        max_num_seqs=2,
        enable_prefix_caching=enable_prefix_caching,
    )
    requests = create_requests(
        num_requests=2,
        num_tokens=512,
        prompt_logprobs=prompt_logprobs,
    )

    # Schedule the first request.
    scheduler.add_request(requests[0])
    scheduler_output0 = scheduler.schedule()
    assert len(scheduler_output0.scheduled_new_reqs) == 1
    assert scheduler_output0.num_scheduled_tokens[
        requests[0].request_id] == 512

    # The first request is still running, so only schedule the second request.
    scheduler.add_request(requests[1])
    scheduler_output1 = scheduler.schedule()
    assert len(scheduler_output1.scheduled_new_reqs) == 1
    assert scheduler_output1.num_scheduled_tokens[
        requests[1].request_id] == 512

    # Model output of the first request.
    model_runner_output = ModelRunnerOutput(
        req_ids=[requests[0].request_id],
        req_id_to_index={requests[0].request_id: 0},
        sampled_token_ids=[[0]],
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    scheduler.update_from_output(scheduler_output0, model_runner_output)

    # Schedule the next step.
    # The first request can be scheduled again while the second
    # request is still running.
    scheduler_output2 = scheduler.schedule()
    assert scheduler_output2.num_scheduled_tokens[requests[0].request_id] == 1

    # Model output of the second request.
    model_runner_output = ModelRunnerOutput(
        req_ids=[requests[1].request_id],
        req_id_to_index={requests[1].request_id: 0},
        sampled_token_ids=[[0]],
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    scheduler.update_from_output(scheduler_output1, model_runner_output)


# Note - these test cases mirror some of those in test_rejection_sampler.py
@pytest.mark.parametrize(
    "spec_tokens,output_tokens,expected",
    [
        ([[1, 2, 3]], [[1, 2, 3, 4]], (1, 3, 3, [1, 1, 1])),  # perfect match
        ([[1, 2, 3]], [[1, 5]], (1, 3, 1, [1, 0, 0])),  # early mismatch
        ([[1, 2], [3]], [[1, 2, 5], [3, 4]],
         (2, 3, 3, [2, 1])),  # multiple sequences
        ([[1]], [[1, 2]], (1, 1, 1, [1])),  # single token sequence
        ([[]], [[5]], (0, 0, 0, [0])),  # empty sequence
        ([[1, 2, 3], [4, 5, 6]], [[1, 2, 7], [4, 8]],
         (2, 6, 3, [2, 1, 0])),  # multiple mismatches
    ])
def test_schedule_spec_decoding_stats(spec_tokens, output_tokens, expected):
    """Test scheduling behavior with speculative decoding.

    This test verifies that:
    1. Speculated tokens get scheduled correctly
    2. Spec decoding stats properly count number of draft and accepted tokens
    """
    num_spec_tokens = max(1, max(len(t) for t in spec_tokens))
    scheduler = create_scheduler(num_speculative_tokens=num_spec_tokens)
    requests = create_requests(num_requests=len(spec_tokens), num_tokens=1)
    req_ids = []
    req_to_index = {}
    for i, request in enumerate(requests):
        scheduler.add_request(request)
        req_ids.append(request.request_id)
        req_to_index[request.request_id] = i

    # Schedule a decode, which will also draft speculative tokens
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == len(requests)
    assert output.total_num_scheduled_tokens == len(requests)
    for i in range(len(requests)):
        req_id = requests[i].request_id
        assert output.num_scheduled_tokens[req_id] == 1
        assert req_id not in output.scheduled_spec_decode_tokens

    model_runner_output = ModelRunnerOutput(
        req_ids=req_ids,
        req_id_to_index=req_to_index,
        sampled_token_ids=[[0] for _ in range(len(requests))],
        spec_token_ids=spec_tokens,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    engine_core_outputs = scheduler.update_from_output(output,
                                                       model_runner_output)

    for i in range(len(requests)):
        running_req = scheduler.running[i]
        # The prompt token
        assert running_req.num_computed_tokens == 1
        # The prompt token and the sampled token
        assert running_req.num_tokens == 2
        # The prompt token, the sampled token, and the speculated tokens
        assert running_req.num_tokens_with_spec == 2 + len(spec_tokens[i])

    # No draft or accepted tokens counted yet
    assert not engine_core_outputs or (
        engine_core_outputs[0].scheduler_stats.spec_decoding_stats is None)

    # Schedule the speculated tokens for validation
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 0
    # The sampled token and speculated tokens
    assert output.total_num_scheduled_tokens == \
        len(requests) + sum(len(ids) for ids in spec_tokens)
    for i in range(len(requests)):
        req_id = requests[i].request_id
        assert output.num_scheduled_tokens[req_id] == 1 + len(spec_tokens[i])
        if spec_tokens[i]:
            assert len(output.scheduled_spec_decode_tokens[req_id]) == \
                len(spec_tokens[i])
        else:
            assert req_id not in output.scheduled_spec_decode_tokens

    model_runner_output = ModelRunnerOutput(
        req_ids=req_ids,
        req_id_to_index=req_to_index,
        sampled_token_ids=output_tokens,
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    engine_core_outputs = scheduler.update_from_output(output,
                                                       model_runner_output)

    scheduler_stats = engine_core_outputs[0].scheduler_stats \
        if engine_core_outputs else None
    if expected[0] == 0:
        assert scheduler_stats.spec_decoding_stats is None
    else:
        assert scheduler_stats.spec_decoding_stats is not None
        stats = scheduler_stats.spec_decoding_stats
        assert stats.num_drafts == expected[0]
        assert stats.num_draft_tokens == expected[1]
        assert stats.num_accepted_tokens == expected[2]
        assert stats.num_accepted_tokens_per_pos == expected[3]


def _assert_right_scheduler_output(
    output: SchedulerOutput,
    num_requests: int,
    expected_num_scheduled_tokens: int,
):
    """Check if SchedulerOutput is correct after remote KV cache hit."""

    # We should inject the kv_connector_metadata.
    assert len(output.kv_connector_metadata.requests) == num_requests

    # Only num_tokens - matched_num_new_tokens should be scheduled.
    for _, num_scheduled_tokens in output.num_scheduled_tokens.items():
        assert num_scheduled_tokens == expected_num_scheduled_tokens


def _assert_right_kv_cache_manager(
    scheduler: Scheduler,
    req_ids: list[str],
    num_tokens: int,
    block_size: int,
    num_requests: int,
    num_total_blocks: int,
):
    """Check whether KVCacheManager is correct after allocate."""

    # Make sure the request stats are right.
    EXPECTED_TOTAL_BLOCKS = num_tokens // block_size
    for req_id in req_ids:
        blocks = (scheduler.kv_cache_manager.coordinator.
                  single_type_managers[0].req_to_blocks[req_id])
        hashes = scheduler.kv_cache_manager.req_to_block_hashes[req_id]
        assert (scheduler.kv_cache_manager.coordinator.single_type_managers[0].
                num_cached_block[req_id] == EXPECTED_TOTAL_BLOCKS)
        assert len(blocks) == EXPECTED_TOTAL_BLOCKS
        assert len(hashes) == EXPECTED_TOTAL_BLOCKS

    # Make sure we actually touched all the blocks.
    BLOCKS_PER_REQ = num_tokens / block_size
    assert (scheduler.kv_cache_manager.block_pool.get_num_free_blocks() ==
            num_total_blocks - num_requests * BLOCKS_PER_REQ)


def _step_until_done(
    scheduler: Scheduler,
    output: SchedulerOutput,
    model_runner_output: ModelRunnerOutput,
):
    """Loop over schedule(), update_from_output() until finished."""

    all_finished = False
    _ = scheduler.update_from_output(output, model_runner_output)
    while not all_finished:
        # Schedule + a few iterations until stopping.
        output = scheduler.schedule()
        assert len(scheduler.running)
        for _, num_scheduled_tokens in output.num_scheduled_tokens.items():
            # We should be in the decode phase now.
            assert num_scheduled_tokens == 1
        assert len(output.kv_connector_metadata.requests) == 0
        ecos = scheduler.update_from_output(output, model_runner_output)[0]
        all_done = True
        for eco in ecos.outputs:
            if eco.finish_reason is None:
                all_done = False
        all_finished = all_done


def test_kv_connector_basic():
    """
    Test whether Scheduler with KVConnector schedules tokens, allocates
    memory, and cleans up requests as expected under normal operation.
    """

    # Setup Scheduler.
    scheduler = create_scheduler(
        enable_prefix_caching=True,
        use_kv_connector=True,
    )
    NUM_TOTAL_BLOCKS = (
        scheduler.kv_cache_manager.block_pool.get_num_free_blocks())
    BLOCK_SIZE = scheduler.cache_config.block_size

    # Mock External Cache Hit.
    NUM_MATCHED_NEW_TOKENS = BLOCK_SIZE * 2
    scheduler.connector.get_num_new_matched_tokens = Mock(name="method")
    scheduler.connector.get_num_new_matched_tokens.return_value = (
        NUM_MATCHED_NEW_TOKENS, False)

    ######################################################
    # FIRST SET OF REQUESTS - External Hit Only
    NUM_REQUESTS = 2
    NUM_TOKENS = NUM_MATCHED_NEW_TOKENS * 2
    MAX_TOKENS = 3
    requests = create_requests(num_requests=NUM_REQUESTS,
                               num_tokens=NUM_TOKENS,
                               max_tokens=MAX_TOKENS)
    req_ids = []
    req_to_index = {}
    for i, request in enumerate(requests):
        scheduler.add_request(request)
        req_ids.append(request.request_id)
        req_to_index[request.request_id] = i

    MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
        req_ids=req_ids,
        req_id_to_index=req_to_index,
        sampled_token_ids=[[1000]] * len(req_ids),
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )

    # Ensure ScheduleOutput is correct.
    output = scheduler.schedule()
    _assert_right_scheduler_output(
        output=output,
        num_requests=NUM_REQUESTS,
        # Just the incremental tokens should be scheduled.
        expected_num_scheduled_tokens=NUM_TOKENS - NUM_MATCHED_NEW_TOKENS,
    )

    # Ensure KVCacheManager is correct.
    _assert_right_kv_cache_manager(scheduler, req_ids, NUM_TOKENS, BLOCK_SIZE,
                                   NUM_REQUESTS, NUM_TOTAL_BLOCKS)

    # Continue Generation until done.
    _step_until_done(scheduler, output, MODEL_RUNNER_OUTPUT)
    _ = scheduler.schedule()
    # Confirm we clean up the memory properly.
    assert scheduler.kv_cache_manager.block_pool.get_num_free_blocks() \
        == NUM_TOTAL_BLOCKS

    ######################################################
    # SECOND SET OF REQUESTS - Local And External Hit
    NUM_TOKENS_PREFIX = NUM_TOKENS
    # We will get a local prefix cache hit for the first
    # NUM_TOKENS_PREFIX tokens since they are used above.
    NUM_TOKENS = NUM_TOKENS_PREFIX * 2
    requests = create_requests(num_requests=NUM_REQUESTS,
                               num_tokens=NUM_TOKENS,
                               max_tokens=MAX_TOKENS)
    req_ids = []
    req_to_index = {}
    for i, request in enumerate(requests):
        scheduler.add_request(request)
        req_ids.append(request.request_id)
        req_to_index[request.request_id] = i

    MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
        req_ids=req_ids,
        req_id_to_index=req_to_index,
        sampled_token_ids=[[1000]] * len(req_ids),
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )

    # We should get a local cache hit of NUM_TOKENS_PREFIX and
    # a remote KV cache hit of NUM_MATCHED_NEW_TOKENS.
    output = scheduler.schedule()
    _assert_right_scheduler_output(
        output=output,
        num_requests=NUM_REQUESTS,
        # Just the incremental tokens after local + remote cache hit.
        expected_num_scheduled_tokens=(NUM_TOKENS - NUM_TOKENS_PREFIX -
                                       NUM_MATCHED_NEW_TOKENS))

    # Ensure KVCacheManager is correct.
    _assert_right_kv_cache_manager(scheduler, req_ids, NUM_TOKENS, BLOCK_SIZE,
                                   NUM_REQUESTS, NUM_TOTAL_BLOCKS)

    # Continue Generation until done.
    _step_until_done(scheduler, output, MODEL_RUNNER_OUTPUT)
    _ = scheduler.schedule()
    # Confirm we clean up the memory properly.
    assert scheduler.kv_cache_manager.block_pool.get_num_free_blocks() \
        == NUM_TOTAL_BLOCKS


def test_kv_connector_unable_to_allocate():
    """
    Test whether scheduler with KVConnector is able to handle
    unable to allocate (run out of blocks in allocate_slots().
    """

    # Setup Scheduler With Mock External Cache Hit.
    BLOCK_SIZE = 4 if not current_platform.is_rocm() else 64
    NUM_BLOCKS = 10
    scheduler = create_scheduler(
        enable_prefix_caching=True,
        use_kv_connector=True,
        block_size=BLOCK_SIZE,
        num_blocks=NUM_BLOCKS,
    )
    NUM_MATCHED_NEW_TOKENS = BLOCK_SIZE * 2
    scheduler.connector.get_num_new_matched_tokens = Mock(name="method")
    scheduler.connector.get_num_new_matched_tokens.return_value = (
        NUM_MATCHED_NEW_TOKENS, False)

    # Create two requests. The second request will not be able to
    # allocate slots because it will not have enough blocks.
    NUM_REQUESTS = 2
    NUM_TOKENS = (NUM_BLOCKS // 2 + 1) * BLOCK_SIZE
    MAX_TOKENS = 2
    requests = create_requests(num_requests=NUM_REQUESTS,
                               num_tokens=NUM_TOKENS,
                               max_tokens=MAX_TOKENS)
    req_ids = []
    req_to_index = {}
    for i, request in enumerate(requests):
        scheduler.add_request(request)
        req_ids.append(request.request_id)
        req_to_index[request.request_id] = i

    MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
        req_ids=req_ids,
        req_id_to_index=req_to_index,
        sampled_token_ids=[[1000]] * len(req_ids),
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )

    # Just one request should be running.
    output = scheduler.schedule()
    _assert_right_scheduler_output(output,
                                   num_requests=1,
                                   expected_num_scheduled_tokens=NUM_TOKENS -
                                   NUM_MATCHED_NEW_TOKENS)
    assert len(scheduler.running) == 1
    assert len(scheduler.waiting) == 1

    # All memory should be freed, with one request waiting.
    _step_until_done(scheduler, output, MODEL_RUNNER_OUTPUT)
    assert scheduler.kv_cache_manager.block_pool.get_num_free_blocks() \
        == NUM_BLOCKS - 1
    assert len(scheduler.running) == 0
    assert len(scheduler.waiting) == 1

    # Just one request should be running.
    output = scheduler.schedule()
    _assert_right_scheduler_output(output,
                                   num_requests=1,
                                   expected_num_scheduled_tokens=NUM_TOKENS -
                                   NUM_MATCHED_NEW_TOKENS)
    assert len(scheduler.running) == 1
    assert len(scheduler.waiting) == 0

    # All memory should be freed, with no requests waiting / running.
    _step_until_done(scheduler, output, MODEL_RUNNER_OUTPUT)
    assert scheduler.kv_cache_manager.block_pool.get_num_free_blocks() \
        == NUM_BLOCKS - 1
    assert len(scheduler.running) == 0
    assert len(scheduler.waiting) == 0


# def test_kv_connector_handles_preemption():
#     """
#     Test whether scheduler with KVConnector is able to handle
#     unable to allocate (run out of blocks in allocate_slots().
#     """

#     # Setup Scheduler With Mock External Cache Hit.
#     BLOCK_SIZE = 2 if not current_platform.is_rocm() else 64
#     # NOTE: there is 1 null block, so this is 6 blocks.
#     NUM_BLOCKS = 7
#     scheduler = create_scheduler(
#         enable_prefix_caching=True,
#         use_kv_connector=True,
#         block_size=BLOCK_SIZE,
#         num_blocks=NUM_BLOCKS,
#     )

#     NUM_MATCHED_NEW_TOKENS = BLOCK_SIZE
#     scheduler.connector.get_num_new_matched_tokens = Mock(name="method")
#     scheduler.connector.get_num_new_matched_tokens.return_value = (
#         NUM_MATCHED_NEW_TOKENS, False)

#     # Create two requests.
#     # Both can be scheduled at first, but the second request
#     # will be preempted and re-scheduled.
#     NUM_REQUESTS = 2
#     NUM_TOKENS = BLOCK_SIZE * 2 + 1
#     MAX_TOKENS = BLOCK_SIZE * 2
#     requests = create_requests(num_requests=NUM_REQUESTS,
#                                num_tokens=NUM_TOKENS,
#                                max_tokens=MAX_TOKENS)
#     req_ids = []
#     req_to_index = {}
#     for i, request in enumerate(requests):
#         scheduler.add_request(request)
#         req_ids.append(request.request_id)
#         req_to_index[request.request_id] = i

#     MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
#         req_ids=req_ids,
#         req_id_to_index=req_to_index,
#         sampled_token_ids=[[1000]] * len(req_ids),
#         spec_token_ids=None,
#         logprobs=None,
#         prompt_logprobs_dict={},
#         pooler_output=[],
#     )

#     # All can be scheduled - 1st token.
#     output = scheduler.schedule()
#     _assert_right_scheduler_output(
#         output,
#         # 2 remote kv cache hits.
#         num_requests=2,
#         expected_num_scheduled_tokens=NUM_TOKENS - NUM_MATCHED_NEW_TOKENS)
#     assert len(scheduler.running) == 2
#     _ = scheduler.update_from_output(output, MODEL_RUNNER_OUTPUT)

#     # All can be scheduled - 2nd token.
#     output = scheduler.schedule()
#     _assert_right_scheduler_output(
#         output,
#         # no connector_metadata
#         num_requests=0,
#         expected_num_scheduled_tokens=1)
#     assert len(scheduler.running) == 2
#     _ = scheduler.update_from_output(output, MODEL_RUNNER_OUTPUT)

#     # This will generate a new block and cause a preemption - 3rd token.
#     output = scheduler.schedule()
#     _assert_right_scheduler_output(
#         output,
#         # no connector_metadata
#         num_requests=0,
#         expected_num_scheduled_tokens=1)
#     assert len(scheduler.running) == 1
#     assert len(scheduler.waiting) == 1
#     _ = scheduler.update_from_output(output, MODEL_RUNNER_OUTPUT)
#     assert len(scheduler.running) == 1
#     assert len(scheduler.waiting) == 1

#     # Only 1 can be scheduled - 4th (and last token).
#     output = scheduler.schedule()
#     _assert_right_scheduler_output(
#         output,
#         # no connector_metadata
#         num_requests=0,
#         expected_num_scheduled_tokens=1)
#     assert len(scheduler.waiting) == 1
#     assert len(scheduler.running) == 1
#     _ = scheduler.update_from_output(output, MODEL_RUNNER_OUTPUT)
#     assert len(scheduler.running) == 0
#     # All memory should be freed since nothing is running.
#     assert scheduler.kv_cache_manager.block_pool.get_num_free_blocks() \
#         == NUM_BLOCKS - 1

#     # Restarts the preempted request - generate 3rd token.
#     # This will have a local and remote cache hit.
#     output = scheduler.schedule()
#     _assert_right_scheduler_output(
#         output,
#         # 1 remote kv_cache hit!
#         num_requests=1,
#         # Only 1 block was preempted and there is a single
#         # remote hit. So only single new token is scheduled.
#         expected_num_scheduled_tokens=1,
#     )
#     assert len(scheduler.running) == 1
#     assert len(scheduler.waiting) == 0
#     _ = scheduler.update_from_output(output, MODEL_RUNNER_OUTPUT)
#     assert len(scheduler.running) == 1
#     assert len(scheduler.waiting) == 0

#     # Only 1 can be scheduled - 4th (and last token).
#     output = scheduler.schedule()
#     _assert_right_scheduler_output(
#         output,
#         # no connector_metadata
#         num_requests=0,
#         expected_num_scheduled_tokens=1)
#     assert len(scheduler.running) == 1
#     _ = scheduler.update_from_output(output, MODEL_RUNNER_OUTPUT)
#     assert len(scheduler.running) == 0
#     # All memory should be freed since nothing is running.
#     assert scheduler.kv_cache_manager.block_pool.get_num_free_blocks() \
#         == NUM_BLOCKS - 1


def make_output(scheduler: Scheduler):
    return ModelRunnerOutput(
        req_ids=[req.request_id for req in scheduler.running],
        req_id_to_index={
            req.request_id: i
            for i, req in enumerate(scheduler.running)
        },
        sampled_token_ids=[[1000]] * len(scheduler.running),
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )


def assert_scheduler_empty(scheduler: Scheduler):
    """Confirm the scheduler is "empty" - i.e. no leaks."""
    # Scheduler Metadata.
    assert len(scheduler.requests) == 0
    assert len(scheduler.waiting) == 0
    assert len(scheduler.running) == 0
    assert len(scheduler.finished_req_ids) == 0

    # EncoderCacheManager.
    assert len(scheduler.encoder_cache_manager.freed) == 0
    assert len(scheduler.encoder_cache_manager.cached) == 0

    # KVCache Manager.
    assert len(scheduler.kv_cache_manager.coordinator.single_type_managers[0].
               req_to_blocks) == 0
    assert len(scheduler.kv_cache_manager.req_to_block_hashes) == 0
    assert len(scheduler.kv_cache_manager.coordinator.single_type_managers[0].
               num_cached_block) == 0
    num_free_blocks = (
        scheduler.kv_cache_manager.block_pool.free_block_queue.num_free_blocks)
    assert num_free_blocks == (
        scheduler.kv_cache_manager.block_pool.num_gpu_blocks - 1)

    # NOTE(rob): just the ref count on blocks will be 0. The hash
    # value, etc will remain since we lazily evict for prefix cache.
    for block in scheduler.kv_cache_manager.block_pool.blocks:
        assert block.ref_cnt == 0
        # assert block._block_hash is None
    # assert (
    #     len(scheduler.kv_cache_manager.block_pool.cached_block_hash_to_block
    #           ) == 0)


def test_memory_leak():
    """Test that we do not have a memory leak."""

    scheduler = create_scheduler(enable_prefix_caching=True)

    NUM_REQUESTS = 5
    NUM_TOKENS = 10
    MAX_TOKENS = 10
    requests = create_requests(num_requests=NUM_REQUESTS,
                               num_tokens=NUM_TOKENS,
                               max_tokens=MAX_TOKENS)

    # Add each request.
    for request in requests:
        scheduler.add_request(request)
        scheduler_output = scheduler.schedule()
        model_runner_output = make_output(scheduler)
        scheduler.update_from_output(scheduler_output, model_runner_output)

    # Iterate until done.
    while True:
        scheduler_output = scheduler.schedule()
        if len(scheduler.running) == 0:
            break
        model_runner_output = make_output(scheduler)
        scheduler.update_from_output(scheduler_output, model_runner_output)

    # Confirm no memory leak.
    assert_scheduler_empty(scheduler)


def create_scheduler_with_priority(
    model: str = "facebook/opt-125m",
    max_num_seqs: int = 16,
    max_num_batched_tokens: int = 8192,
    enable_prefix_caching: Optional[bool] = None,
    long_prefill_token_threshold: int = 0,
    disable_chunked_mm_input: bool = False,
    use_kv_connector: bool = False,
    num_blocks: int = 10000,
    block_size: int = 16,
    max_model_len: Optional[int] = None,
    num_speculative_tokens: Optional[int] = None,
) -> Scheduler:
    '''Create scheduler with priority policy enabled.

    Args:
      model: model under test
      max_num_seqs: max sequences to schedule
      max_num_batch_tokens: max num tokens to batch
      enable_prefix_caching: optionally force APC config
                             (True/False) or use default
                             (None)

    Returns:
      {class}`Scheduler` instance with priority scheduling
    '''
    if max_model_len is None:
        max_model_len = max_num_batched_tokens
    scheduler_config = SchedulerConfig(
        max_num_seqs=max_num_seqs,
        max_num_batched_tokens=max_num_batched_tokens,
        max_model_len=max_model_len,
        long_prefill_token_threshold=long_prefill_token_threshold,
        disable_chunked_mm_input=disable_chunked_mm_input,
        enable_chunked_prefill=True,
        policy="priority",  # Enable priority scheduling
    )
    model_config = ModelConfig(
        model=model,
        task="auto",
        tokenizer=model,
        tokenizer_mode="auto",
        trust_remote_code=True,
        dtype="float16",
        seed=42,
    )
    # Cache config, optionally force APC
    kwargs_cache = ({} if enable_prefix_caching is None else {
        'enable_prefix_caching': enable_prefix_caching
    })
    cache_config = CacheConfig(
        block_size=block_size,
        gpu_memory_utilization=0.9,
        swap_space=0,
        cache_dtype="auto",
        **kwargs_cache,
    )
    kv_transfer_config = KVTransferConfig(
        kv_connector="SharedStorageConnector",
        kv_role="kv_both",
        kv_connector_extra_config={"shared_storage_path": "local_storage"},
    ) if use_kv_connector else None

    speculative_config: Optional[SpeculativeConfig] = None
    if num_speculative_tokens is not None:
        speculative_config = SpeculativeConfig(
            model="ngram", num_speculative_tokens=num_speculative_tokens)

    vllm_config = VllmConfig(
        scheduler_config=scheduler_config,
        model_config=model_config,
        cache_config=cache_config,
        kv_transfer_config=kv_transfer_config,
        speculative_config=speculative_config,
    )
    kv_cache_config = KVCacheConfig(
        num_blocks=num_blocks,  # A large number of blocks to hold all requests
        kv_cache_tensors=[],
        kv_cache_groups=[
            KVCacheGroupSpec(['layer'],
                             FullAttentionSpec(block_size, 1, 1, torch.float32,
                                               False))
        ],
    )
    cache_config.num_gpu_blocks = num_blocks
    return Scheduler(
        vllm_config=vllm_config,
        kv_cache_config=kv_cache_config,
        log_stats=True,
        structured_output_manager=StructuredOutputManager(vllm_config),
    )


def create_requests_with_priority(
        num_requests: int,
        priorities: list[int],
        arrival_times: Optional[list[float]] = None,
        num_tokens: int = 10,
        mm_positions: Optional[list[PlaceholderRange]] = None,
        max_tokens: int = 16,
        stop_token_ids: Optional[list[int]] = None,
        prompt_logprobs: Optional[int] = None):
    """Create requests with specified priorities and arrival times."""
    assert len(priorities) == num_requests
    if arrival_times is not None:
        assert len(arrival_times) == num_requests
    else:
        arrival_times = [float(i) for i in range(num_requests)]

    sampling_params = SamplingParams(ignore_eos=False,
                                     max_tokens=max_tokens,
                                     stop_token_ids=stop_token_ids,
                                     prompt_logprobs=prompt_logprobs)
    requests = []
    for i in range(num_requests):
        if mm_positions is not None:
            mm_position = mm_positions[i]
            mm_inputs = [MultiModalKwargs({})] * len(mm_position)
        else:
            mm_position = None
            mm_inputs = None
        request = Request(
            request_id=f"{i}",
            prompt_token_ids=[i] * num_tokens,
            sampling_params=sampling_params,
            pooling_params=None,
            multi_modal_inputs=mm_inputs,
            multi_modal_placeholders=mm_position,
            multi_modal_hashes=None,
            eos_token_id=EOS_TOKEN_ID,
            arrival_time=arrival_times[i],
            priority=priorities[i],
        )
        requests.append(request)
    return requests


def test_priority_scheduling_basic_ordering():
    """Test that requests are scheduled in priority order
    (lower value = higher priority)."""
    scheduler = create_scheduler_with_priority()

    # Create requests with different priorities
    # Priority 0 (highest), 1, 2 (lowest)
    priorities = [2, 0, 1]  # Add in non-priority order
    arrival_times = [1.0, 2.0, 3.0]  # All different arrival times
    requests = create_requests_with_priority(num_requests=3,
                                             priorities=priorities,
                                             arrival_times=arrival_times)

    # Add requests in non-priority order
    for request in requests:
        scheduler.add_request(request)

    # Schedule and verify priority order
    output = scheduler.schedule()

    # Should schedule all requests since they fit in budget
    assert len(output.scheduled_new_reqs) == 3

    # Verify they are scheduled in priority order:
    # req_1 (priority 0), req_2 (priority 1), req_0 (priority 2)
    scheduled_req_ids = [req.req_id for req in output.scheduled_new_reqs]
    assert scheduled_req_ids == ["1", "2", "0"]


def test_priority_scheduling_arrival_time_tiebreaker():
    """Test that arrival time is used
    as tiebreaker when priorities are equal."""
    scheduler = create_scheduler_with_priority()

    # Create requests with same priority but different arrival times
    priorities = [1, 1, 1]  # All same priority
    arrival_times = [3.0, 1.0, 2.0]  # Different arrival times
    requests = create_requests_with_priority(num_requests=3,
                                             priorities=priorities,
                                             arrival_times=arrival_times)

    # Add requests in non-arrival order
    for request in requests:
        scheduler.add_request(request)

    # Schedule and verify arrival time order
    output = scheduler.schedule()

    # Should schedule all requests since they fit in budget
    assert len(output.scheduled_new_reqs) == 3

    # Verify they are scheduled in arrival time order:
    # req_1 (1.0), req_2 (2.0), req_0 (3.0)
    scheduled_req_ids = [req.req_id for req in output.scheduled_new_reqs]
    assert scheduled_req_ids == ["1", "2", "0"]


def test_priority_scheduling_mixed_priority_and_arrival():
    """Test priority scheduling with mixed priorities and arrival times."""
    scheduler = create_scheduler_with_priority()

    # Create requests with mixed priorities and arrival times
    priorities = [2, 1, 1, 0]  # Mixed priorities
    arrival_times = [1.0, 3.0, 2.0, 4.0]  # Mixed arrival times
    requests = create_requests_with_priority(num_requests=4,
                                             priorities=priorities,
                                             arrival_times=arrival_times)

    # Add requests
    for request in requests:
        scheduler.add_request(request)

    # Schedule and verify order
    output = scheduler.schedule()

    # Should schedule all requests since they fit in budget
    assert len(output.scheduled_new_reqs) == 4

    # Expected order:
    # 1. req_3 (priority 0, arrival 4.0)
    # 2. req_2 (priority 1, arrival 2.0) - earlier arrival than req_1
    # 3. req_1 (priority 1, arrival 3.0)
    # 4. req_0 (priority 2, arrival 1.0)
    scheduled_req_ids = [req.req_id for req in output.scheduled_new_reqs]
    assert scheduled_req_ids == ["3", "2", "1", "0"]


def test_priority_scheduling_preemption():
    """Test that priority scheduling preempts
    lower priority requests when memory is constrained."""
    # Create scheduler with very limited memory to force preemption
    scheduler = create_scheduler_with_priority(
        max_num_seqs=3,  # Allow multiple requests
        max_num_batched_tokens=200,
        num_blocks=6,  # Very limited blocks to force memory pressure
        block_size=16,  # Standard block size
    )

    # Create initial low-priority requests that will consume most memory
    low_priority_requests = create_requests_with_priority(
        num_requests=2,
        priorities=[5, 5],  # Low priority
        arrival_times=[1.0, 2.0],
        num_tokens=30  # Large enough to consume significant memory
    )

    # Add and schedule low priority requests
    for request in low_priority_requests:
        scheduler.add_request(request)

    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 2

    # Simulate model execution to move requests to running state
    model_output = ModelRunnerOutput(
        req_ids=[req.request_id for req in low_priority_requests],
        req_id_to_index={
            req.request_id: i
            for i, req in enumerate(low_priority_requests)
        },
        sampled_token_ids=[[100] for _ in low_priority_requests],
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    scheduler.update_from_output(output, model_output)

    # Verify both requests are running
    assert len(scheduler.running) == 2

    # Now add a high-priority request that requires memory allocation
    # This should trigger preemption due to memory constraints
    high_priority_request = create_requests_with_priority(
        num_requests=1,
        priorities=[0],  # High priority
        arrival_times=[3.0],
        num_tokens=30  # Large enough to require significant memory
    )[0]

    scheduler.add_request(high_priority_request)

    # Schedule again - this should trigger
    # preemption when trying to allocate memory
    output = scheduler.schedule()

    # Due to the scheduler's design, if preemption happens
    # during running request scheduling,
    # waiting requests won't be scheduled in the same step
    # Let's check if preemption occurred by looking at the waiting queue

    # If preemption happened, we should see requests in the
    # waiting queue
    if len(scheduler.waiting) > 1:  # high priority + preempted request
        # Preemption occurred - verify the high priority request
        # gets scheduled next
        output2 = scheduler.schedule()
        assert len(output2.scheduled_new_reqs) == 1
        # High priority request
        assert output2.scheduled_new_reqs[0].req_id == "0"
    else:
        # No preemption needed - all requests fit
        # This is also valid behavior if memory allows
        assert len(output.scheduled_new_reqs) == 1
        # High priority request
        assert output.scheduled_new_reqs[0].req_id == "0"


def test_priority_scheduling_no_preemption_when_space_available():
    """Test that preemption doesn't happen
    when there's space for new requests."""
    scheduler = create_scheduler_with_priority(
        max_num_seqs=3,  # Allow 3 concurrent requests
        max_num_batched_tokens=200,  # Sufficient token budget
    )

    # Add two low-priority running requests
    low_priority_requests = create_requests_with_priority(
        num_requests=2,
        priorities=[5, 5],
        arrival_times=[1.0, 2.0],
        num_tokens=30)

    for request in low_priority_requests:
        scheduler.add_request(request)

    output = scheduler.schedule()
    model_output = ModelRunnerOutput(
        req_ids=[req.request_id for req in low_priority_requests],
        req_id_to_index={
            req.request_id: i
            for i, req in enumerate(low_priority_requests)
        },
        sampled_token_ids=[[100] for _ in low_priority_requests],
        spec_token_ids=None,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=[],
    )
    scheduler.update_from_output(output, model_output)

    # Add high-priority request
    high_priority_request = create_requests_with_priority(num_requests=1,
                                                          priorities=[0],
                                                          arrival_times=[3.0],
                                                          num_tokens=30)[0]

    scheduler.add_request(high_priority_request)

    # Schedule - should not preempt since there's space
    output = scheduler.schedule()

    # Should schedule the new request without preemption
    assert len(output.scheduled_new_reqs) == 1
    assert len(scheduler.running) == 3  # All three requests running
    assert len(scheduler.waiting) == 0  # No requests waiting


def test_priority_scheduling_preemption_victim_selection():
    """Test that the correct victim is selected for
    preemption based on priority and arrival time."""
    # This test verifies the priority-based victim selection logic
    # by checking the waiting queue order after adding requests with different
    # priorities
    scheduler = create_scheduler_with_priority(
        max_num_seqs=1,  # Force sequential processing to test priority order
    )

    # Create requests with different priorities
    requests = create_requests_with_priority(
        num_requests=3,
        priorities=[3, 2, 0],  # Different priorities: low, medium, high
        arrival_times=[1.0, 2.0, 3.0],
        num_tokens=10)

    # Add all requests
    for request in requests:
        scheduler.add_request(request)

    # Schedule - should only schedule the highest priority request
    # (req_2, priority 0)
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 1
    assert output.scheduled_new_reqs[0].req_id == "2"  # Highest priority

    # Verify the waiting queue has the remaining requests in priority order
    assert len(scheduler.waiting) == 2

    # Extract waiting requests and verify priority order
    waiting_requests = list(scheduler.waiting)

    waiting_priorities = [req.priority for req in waiting_requests]
    waiting_req_ids = [req.request_id for req in waiting_requests]

    # Should be req_1 (priority 2) then req_0 (priority 3)
    assert waiting_priorities == [2, 3]
    assert waiting_req_ids == ["1", "0"]


def test_priority_scheduling_equal_priority_preemption():
    """Test arrival time tiebreaker when requests have equal priority."""
    # This test verifies that arrival time is used as a tiebreaker for equal
    # priorities
    scheduler = create_scheduler_with_priority(
        max_num_seqs=1,  # Force sequential processing
    )

    # Create requests with same priority but different arrival times
    requests = create_requests_with_priority(
        num_requests=3,
        priorities=[2, 2, 2],  # Same priority
        arrival_times=[3.0, 1.0, 2.0],  # Different arrival times
        num_tokens=10)

    # Add all requests
    for request in requests:
        scheduler.add_request(request)

    # Schedule - should schedule the request with earliest arrival time
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 1
    assert output.scheduled_new_reqs[0].req_id == "1"  # Earliest arrival (1.0)

    # Verify the waiting queue has remaining requests in arrival time order
    assert len(scheduler.waiting) == 2

    # Extract waiting requests and verify arrival time order
    waiting_requests = list(scheduler.waiting)

    waiting_arrival_times = [req.arrival_time for req in waiting_requests]
    waiting_req_ids = [req.request_id for req in waiting_requests]

    # Should be req_2 (arrival 2.0) then req_0 (arrival 3.0)
    assert waiting_arrival_times == [2.0, 3.0]
    assert waiting_req_ids == ["2", "0"]


def test_priority_scheduling_waiting_queue_order():
    """Test that the waiting queue maintains priority order."""
    scheduler = create_scheduler_with_priority(
        max_num_seqs=1,  # Only one request can run at a time
    )

    # Create multiple requests with different priorities
    requests = create_requests_with_priority(
        num_requests=4,
        priorities=[3, 1, 2, 0],  # Mixed priorities
        arrival_times=[1.0, 2.0, 3.0, 4.0],
        num_tokens=10)

    # Add all requests
    for request in requests:
        scheduler.add_request(request)

    # Schedule - should only schedule the highest priority request
    # (req_3, priority 0)
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 1
    assert output.scheduled_new_reqs[0].req_id == "3"

    # Verify waiting queue has remaining requests in priority order
    assert len(scheduler.waiting) == 3

    # Extract requests from waiting queue
    # (it's a heap, so we need to pop to see order)
    waiting_requests = list(scheduler.waiting)

    waiting_priorities = [req.priority for req in waiting_requests]
    waiting_req_ids = [req.request_id for req in waiting_requests]

    # Should be ordered by priority: req_1 (1), req_2 (2), req_0 (3)
    assert waiting_req_ids == ["1", "2", "0"]
    assert waiting_priorities == [1, 2, 3]


def test_priority_scheduling_fcfs_fallback():
    """Test that FCFS behavior is maintained when all
    requests have same priority."""
    scheduler = create_scheduler_with_priority()

    # Create requests with same priority but different arrival times
    priorities = [1, 1, 1, 1]  # All same priority
    arrival_times = [4.0, 1.0, 3.0, 2.0]  # Different arrival times
    requests = create_requests_with_priority(num_requests=4,
                                             priorities=priorities,
                                             arrival_times=arrival_times)

    # Add requests
    for request in requests:
        scheduler.add_request(request)

    # Schedule
    output = scheduler.schedule()

    # Should schedule all requests in arrival time order
    assert len(output.scheduled_new_reqs) == 4
    scheduled_req_ids = [req.req_id for req in output.scheduled_new_reqs]

    # Expected order by arrival time:
    # req_1 (1.0), req_3 (2.0), req_2 (3.0), req_0 (4.0)
    assert scheduled_req_ids == ["1", "3", "2", "0"]


def test_priority_scheduling_with_limited_slots():
    """Test priority scheduling when max_num_seqs limits concurrent requests."""
    scheduler = create_scheduler_with_priority(
        max_num_seqs=2,  # Only allow 2 concurrent requests
        max_num_batched_tokens=1000,  # Plenty of token budget
    )

    # Create requests with different priorities
    requests = create_requests_with_priority(
        num_requests=4,
        priorities=[3, 1, 2, 0],  # Mixed priorities
        arrival_times=[1.0, 2.0, 3.0, 4.0],
        num_tokens=10)

    # Add all requests
    for request in requests:
        scheduler.add_request(request)

    # Schedule - should only schedule the 2 highest priority requests
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 2

    # Should schedule req_3 (priority 0) and req_1 (priority 1)
    scheduled_req_ids = [req.req_id for req in output.scheduled_new_reqs]
    assert "3" in scheduled_req_ids  # Priority 0
    assert "1" in scheduled_req_ids  # Priority 1

    # Remaining requests should be in waiting queue in priority order
    assert len(scheduler.waiting) == 2

    # Extract waiting requests and verify order
    waiting_requests = list(scheduler.waiting)
    waiting_priorities = [req.priority for req in waiting_requests]
    waiting_req_ids = [req.request_id for req in waiting_requests]

    # Should be req_2 (priority 2) then req_0 (priority 3)
    assert waiting_priorities == [2, 3]
    assert waiting_req_ids == ["2", "0"]


def test_priority_scheduling_heap_property():
    """Test that the waiting queue maintains heap
    property for priority scheduling."""
    scheduler = create_scheduler_with_priority(
        max_num_seqs=1,  # Only one request can run at a time
    )

    # Add requests in random priority order
    priorities = [5, 1, 8, 3, 2, 7, 4, 6]
    arrival_times = [float(i) for i in range(len(priorities))]
    requests = create_requests_with_priority(num_requests=len(priorities),
                                             priorities=priorities,
                                             arrival_times=arrival_times,
                                             num_tokens=10)

    # Add all requests
    for request in requests:
        scheduler.add_request(request)

    # Schedule one request at a time and verify priority order
    scheduled_priorities = []

    while scheduler.waiting:
        output = scheduler.schedule()
        if output.scheduled_new_reqs:
            req = output.scheduled_new_reqs[0]
            scheduled_priorities.append(requests[int(req.req_id)].priority)

            # Simulate completion to make room for next request
            model_output = ModelRunnerOutput(
                req_ids=[req.req_id],
                req_id_to_index={req.req_id: 0},
                sampled_token_ids=[[100]],
                spec_token_ids=None,
                logprobs=None,
                prompt_logprobs_dict={},
                pooler_output=[],
            )
            scheduler.update_from_output(output, model_output)

            # Finish the request to make room for the next one
            scheduler.finish_requests(req.req_id,
                                      RequestStatus.FINISHED_STOPPED)

    # Verify requests were scheduled in priority order (lowest value first)
    expected_priorities = sorted(priorities)
    assert scheduled_priorities == expected_priorities


def test_schedule_skip_tokenizer_init():
    scheduler = create_scheduler(skip_tokenizer_init=True)
    requests = create_requests(num_requests=5)
    for request in requests:
        scheduler.add_request(request)
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == len(requests)
    assert output.grammar_bitmask is None


def test_schedule_skip_tokenizer_init_structured_output_request():
    scheduler = create_scheduler(skip_tokenizer_init=True)
    guided_params = GuidedDecodingParams(regex="[0-9]+")
    sampling_params = SamplingParams(
        ignore_eos=False,
        max_tokens=16,
        guided_decoding=guided_params,
    )
    request = Request(
        request_id="0",
        prompt_token_ids=[0, 1],
        multi_modal_inputs=None,
        multi_modal_hashes=None,
        multi_modal_placeholders=None,
        sampling_params=sampling_params,
        pooling_params=None,
        eos_token_id=EOS_TOKEN_ID,
        structured_output_request=StructuredOutputRequest(sampling_params),
    )
    scheduler.add_request(request)
    output = scheduler.schedule()
    assert len(output.scheduled_new_reqs) == 0
    assert len(scheduler.running) == 0
    assert len(scheduler.waiting) == 1
