### :title Runtime Configuration Examples
### :order 6
### :section Customization

import argparse

from tensorrt_llm import LLM, SamplingParams
from tensorrt_llm.llmapi import CudaGraphConfig, KvCacheConfig


def example_cuda_graph_config():
    """
    Example demonstrating CUDA graph configuration for performance optimization.

    CUDA graphs help with:
    - Reduced kernel launch overhead
    - Better GPU utilization
    - Improved throughput for repeated operations
    """
    print("\n=== CUDA Graph Configuration Example ===")

    cuda_graph_config = CudaGraphConfig(
        batch_sizes=[1, 2, 4],
        enable_padding=True,
    )

    llm = LLM(
        model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        cuda_graph_config=cuda_graph_config,  # Enable CUDA graphs
        max_batch_size=4,
        max_seq_len=512,
        kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.8,
                                      enable_block_reuse=True))

    prompts = [
        "Hello, my name is",
        "The capital of France is",
        "The future of AI is",
    ]

    sampling_params = SamplingParams(max_tokens=50, temperature=0.8, top_p=0.95)

    # This should benefit from CUDA graphs
    outputs = llm.generate(prompts, sampling_params)
    for output in outputs:
        print(f"Prompt: {output.prompt}")
        print(f"Generated: {output.outputs[0].text}")
        print()


def example_kv_cache_config():
    print("\n=== KV Cache Configuration Example ===")
    print("\n1. KV Cache Configuration:")

    llm_advanced = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
                       max_batch_size=8,
                       max_seq_len=1024,
                       kv_cache_config=KvCacheConfig(
                           free_gpu_memory_fraction=0.85,
                           enable_block_reuse=True))

    prompts = [
        "Hello, my name is",
        "The capital of France is",
        "The future of AI is",
    ]

    outputs = llm_advanced.generate(prompts)
    for i, output in enumerate(outputs):
        print(f"Query {i+1}: {output.prompt}")
        print(f"Answer: {output.outputs[0].text[:100]}...")
        print()


def main():
    """
    Main function to run all runtime configuration examples.
    """
    parser = argparse.ArgumentParser(
        description="Runtime Configuration Examples")
    parser.add_argument("--example",
                        type=str,
                        choices=["kv_cache", "cuda_graph", "all"],
                        default="all",
                        help="Which example to run")

    args = parser.parse_args()

    if args.example == "kv_cache" or args.example == "all":
        example_kv_cache_config()

    if args.example == "cuda_graph" or args.example == "all":
        example_cuda_graph_config()


if __name__ == "__main__":
    main()
