import os
import sys
import unittest

import pytest
import torch

from tensorrt_llm import LLM, SamplingParams
from tensorrt_llm.llmapi import (CudaGraphConfig, DraftTargetDecodingConfig,
                                 KvCacheConfig)

sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from utils.llm_data import llm_models_root
from utils.util import similar


@pytest.mark.parametrize("use_cuda_graph,attn_backend",
                         [[False, "TRTLLM"], [True, "TRTLLM"]])
@pytest.mark.high_cuda_memory
def test_llama_draft_target(use_cuda_graph: bool, attn_backend: str):
    total_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
    if total_mem_gb < 60:
        pytest.skip("Not enough memory to load target model")

    models_path = llm_models_root()
    draft_model_dir = f"{models_path}/llama-3.1-model/Llama-3.1-8B-Instruct"
    target_model_dir = f"{models_path}/llama-3.1-model/Llama-3.1-8B-Instruct"

    max_batch_size = 2
    max_draft_len = 4
    kv_cache_config = KvCacheConfig(enable_block_reuse=False)
    cuda_graph_config = CudaGraphConfig(
        batch_sizes=[1]) if use_cuda_graph else None

    llm_common_config = dict(
        model=target_model_dir,
        backend='pytorch',
        attn_backend=attn_backend,
        disable_overlap_scheduler=True,
        cuda_graph_config=cuda_graph_config,
        max_batch_size=max_batch_size,
        kv_cache_config=kv_cache_config,
        max_num_tokens=2048,
        use_torch_sampler=True,
    )

    spec_config = DraftTargetDecodingConfig(
        max_draft_len=max_draft_len,
        speculative_model_dir=draft_model_dir,
    )

    prompts = [
        "The capital of France is",
        "The president of the United States is",
    ]
    sampling_params = SamplingParams(max_tokens=32)

    llm_spec = LLM(**llm_common_config, speculative_config=spec_config)
    results_spec = llm_spec.generate(prompts, sampling_params)
    generated_text_spec = [result.outputs[0].text for result in results_spec]
    llm_spec.shutdown()

    llm_ref = LLM(**llm_common_config)
    results_ref = llm_ref.generate(prompts, sampling_params)
    generated_text_ref = [result.outputs[0].text for result in results_ref]
    llm_ref.shutdown()

    for text_spec, text_ref in zip(generated_text_spec, generated_text_ref):
        # The spec decode algorithm currently guarantees identical results
        assert similar(text_spec, text_ref)


if __name__ == "__main__":
    unittest.main()
