"""
This example shows how to use the multi-LoRA functionality for offline
inference. Requires HuggingFace credentials for access to Llama2.
"""

from typing import List, Optional, Tuple

from huggingface_hub import snapshot_download

from aphrodite import (AphroditeEngine, EngineArgs, RequestOutput,
                       SamplingParams)
from aphrodite.lora.request import LoRARequest


def create_test_prompts(lora_path: str) -> List[Tuple[str, SamplingParams]]:
    """Create a list of test prompts with their sampling parameters.
    
    2 requests for base model, 4 requests for the LoRA. We define 2
    different LoRA adapters (using the same model for demo purposes).
    Since we also set `max_loras=1`, the expectation is that the requests
    with the second LoRA adapter will be ran after all requests with the
    first adapter have finished.
    """
    return [
        (
            "A robot may not injure a human being",
            SamplingParams(
                temperature=0.0,
                # logprobs=1,
                prompt_logprobs=1,
                max_tokens=128),
            None),
        ("To be or not to be,",
         SamplingParams(temperature=0.8,
                        top_k=5,
                        presence_penalty=0.2,
                        max_tokens=128), None),
        (
            """[user] Write a SQL query to answer the question based on the
            table schema.\n\n context: CREATE TABLE table_name_74
            (icao VARCHAR, airport VARCHAR)\n\n
            question: Name the ICAO for lilongwe
            international airport [/user] [assistant]""",
            SamplingParams(
                temperature=0.0,
                # logprobs=1,
                prompt_logprobs=1,
                max_tokens=128,
                stop_token_ids=[32003]),
            LoRARequest("l2-lora-test", 1, lora_path)),
        ("""[user] Write a SQL query to answer the question based on the table
         schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR,
         elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector
         what is under nationality? [/user] [assistant]""",
         SamplingParams(n=3,
                        best_of=3,
                        temperature=0.8,
                        max_tokens=128,
                        stop_token_ids=[32003]),
         LoRARequest("l2-lora-test", 1, lora_path)),
        (
            """[user] Write a SQL query to answer the question based on the
            table schema.\n\n context: CREATE TABLE table_name_74 (icao
            VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe
            international airport [/user] [assistant]""",
            SamplingParams(
                temperature=0.0,
                # logprobs=1,
                prompt_logprobs=1,
                max_tokens=128,
                stop_token_ids=[32003]),
            LoRARequest("l2-lora-test2", 2, lora_path)),
        ("""[user] Write a SQL query to answer the question based on the table
         schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR,
         elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector
         what is under nationality? [/user] [assistant]""",
         SamplingParams(n=3,
                        best_of=3,
                        temperature=0.9,
                        max_tokens=128,
                        stop_token_ids=[32003]),
         LoRARequest("l2-lora-test", 1, lora_path)),
    ]  # type: ignore


def process_requests(engine: AphroditeEngine,
                     test_prompts: List[Tuple[str, SamplingParams,
                                              Optional[LoRARequest]]]):
    """Continuously process a list of prompts and handle the outputs."""
    request_id = 0

    while test_prompts or engine.has_unfinished_requests():
        if test_prompts:
            prompt, sampling_params, lora_request = test_prompts.pop(0)
            engine.add_request(str(request_id),
                               prompt,
                               sampling_params,
                               lora_request=lora_request)
            request_id += 1

        request_outputs: List[RequestOutput] = engine.step()

        for request_output in request_outputs:
            if request_output.finished:
                print(request_output)


def initialize_engine() -> AphroditeEngine:
    """Initialize the AphroditeEngine."""
    # max_loras: controls the number of LoRAs that can be used in the same
    #   batch. Larger numbers will cause higher memory usage, as each LoRA
    #   slot requires its own preallocated tensor.
    # max_lora_rank: controls the maximum supported rank of all LoRAs. Larger
    #   numbers will cause higher memory usage. If you know that all LoRAs will
    #   use the same rank, it is recommended to set this as low as possible.
    # max_cpu_loras: controls the size of the CPU LoRA cache.
    engine_args = EngineArgs(model="NousResearch/Llama-2-7b-hf",
                             enable_lora=True,
                             max_loras=1,
                             max_lora_rank=8,
                             max_cpu_loras=2,
                             max_num_seqs=256)
    return AphroditeEngine.from_engine_args(engine_args)


def main():
    """Main function that sets up and runs the prompt processing."""
    engine = initialize_engine()
    lora_path = snapshot_download(repo_id="alpindale/l2-lora-test")
    test_prompts = create_test_prompts(lora_path)
    process_requests(engine, test_prompts)


if __name__ == '__main__':
    main()