# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import weakref
import os

import pytest

from vllm import LLM, SamplingParams
from vllm.distributed import cleanup_dist_env_and_memory
from ...utils import models_path_prefix

MODEL_NAME = os.path.join(models_path_prefix, "distilbert/distilgpt2")

PROMPTS = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]

TOKEN_IDS = [
    [0],
    [0, 1],
    [0, 2, 1],
    [0, 3, 1, 2],
]


@pytest.fixture(scope="module")
def llm():
    # pytest caches the fixture so we use weakref.proxy to
    # enable garbage collection
    llm = LLM(
        model=MODEL_NAME,
        max_num_batched_tokens=4096,
        tensor_parallel_size=1,
        gpu_memory_utilization=0.10,
        enforce_eager=True,
    )

    yield weakref.proxy(llm)

    del llm

    cleanup_dist_env_and_memory()


@pytest.mark.skip_global_cleanup
def test_multiple_sampling_params(llm: LLM):
    sampling_params = [
        SamplingParams(temperature=0.01, top_p=0.95),
        SamplingParams(temperature=0.3, top_p=0.95),
        SamplingParams(temperature=0.7, top_p=0.95),
        SamplingParams(temperature=0.99, top_p=0.95),
    ]

    # Multiple SamplingParams should be matched with each prompt
    outputs = llm.generate(PROMPTS, sampling_params=sampling_params)
    assert len(PROMPTS) == len(outputs)

    # Exception raised, if the size of params does not match the size of prompts
    with pytest.raises(ValueError):
        outputs = llm.generate(PROMPTS, sampling_params=sampling_params[:3])

    # Single SamplingParams should be applied to every prompt
    single_sampling_params = SamplingParams(temperature=0.3, top_p=0.95)
    outputs = llm.generate(PROMPTS, sampling_params=single_sampling_params)
    assert len(PROMPTS) == len(outputs)

    # sampling_params is None, default params should be applied
    outputs = llm.generate(PROMPTS, sampling_params=None)
    assert len(PROMPTS) == len(outputs)


def test_multiple_priority(llm: LLM):
    # Generate works when priority is None
    outputs = llm.generate(PROMPTS, sampling_params=None, priority=None)
    assert len(PROMPTS) == len(outputs)

    # Generate works when length of priority is same as the len(PROMPTS)
    outputs = llm.generate(PROMPTS, sampling_params=None, priority=[0] * len(PROMPTS))
    assert len(PROMPTS) == len(outputs)

    # Exception raised, if the length of priority does not match the length of prompts
    with pytest.raises(ValueError):
        outputs = llm.generate(
            PROMPTS, sampling_params=None, priority=[0] * (len(PROMPTS) - 1)
        )

    # Exception raised, if the priority list is empty
    with pytest.raises(ValueError):
        outputs = llm.generate(PROMPTS, sampling_params=None, priority=[])


def test_max_model_len():
    max_model_len = 20
    llm = LLM(
        model=MODEL_NAME,
        max_model_len=max_model_len,
        gpu_memory_utilization=0.10,
        enforce_eager=True,  # reduce test time
    )
    sampling_params = SamplingParams(max_tokens=max_model_len + 10)
    outputs = llm.generate(PROMPTS, sampling_params)
    for output in outputs:
        num_total_tokens = len(output.prompt_token_ids) + len(
            output.outputs[0].token_ids
        )
        # Total tokens must not exceed max_model_len.
        # It can be less if generation finishes due to other reasons (e.g., EOS)
        # before reaching the absolute model length limit.
        assert num_total_tokens <= max_model_len


def test_log_stats():
    llm = LLM(
        model=MODEL_NAME,
        disable_log_stats=False,
        gpu_memory_utilization=0.10,
        enforce_eager=True,  # reduce test time
    )
    outputs = llm.generate(PROMPTS, sampling_params=None)

    # disable_log_stats is False, every output should have metrics
    assert all(output.metrics is not None for output in outputs)
