"""
Hugging face LLM Interface Module
==========================

This module provides interfaces for interacting with Hugging face's language models,
including text generation and embedding capabilities.

Author: Lightrag team
Created: 2024-01-24
License: MIT License

Copyright (c) 2024 Lightrag

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

Version: 1.0.0

Change Log:
- 1.0.0 (2024-01-24): Initial release
    * Added async chat completion support
    * Added embedding generation
    * Added stream response capability

Dependencies:
    - transformers
    - numpy
    - pipmaster
    - Python >= 3.10

Usage:
    from llm_interfaces.hf import hf_model_complete, hf_embed
"""

__version__ = "1.0.0"
__author__ = "lightrag Team"
__status__ = "Production"

import copy
import os
import pipmaster as pm  # Pipmaster for dynamic library install

# install specific modules
if not pm.is_installed("transformers"):
    pm.install("transformers")
if not pm.is_installed("torch"):
    pm.install("torch")
if not pm.is_installed("tenacity"):
    pm.install("tenacity")

from transformers import AutoTokenizer, AutoModelForCausalLM
from functools import lru_cache
from tenacity import (
    retry,
    stop_after_attempt,
    wait_exponential,
    retry_if_exception_type,
)
from lightrag.exceptions import (
    APIConnectionError,
    RateLimitError,
    APITimeoutError,
)
from lightrag.utils import (
    locate_json_string_body_from_string,
)
import torch
import numpy as np

os.environ["TOKENIZERS_PARALLELISM"] = "false"


@lru_cache(maxsize=1)
def initialize_hf_model(model_name):
    hf_tokenizer = AutoTokenizer.from_pretrained(
        model_name, device_map="auto", trust_remote_code=True
    )
    hf_model = AutoModelForCausalLM.from_pretrained(
        model_name, device_map="auto", trust_remote_code=True
    )
    if hf_tokenizer.pad_token is None:
        hf_tokenizer.pad_token = hf_tokenizer.eos_token

    return hf_model, hf_tokenizer


@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10),
    retry=retry_if_exception_type(
        (RateLimitError, APIConnectionError, APITimeoutError)
    ),
)
async def hf_model_if_cache(
    model,
    prompt,
    system_prompt=None,
    history_messages=[],
    **kwargs,
) -> str:
    model_name = model
    hf_model, hf_tokenizer = initialize_hf_model(model_name)
    messages = []
    if system_prompt:
        messages.append({"role": "system", "content": system_prompt})
    messages.extend(history_messages)
    messages.append({"role": "user", "content": prompt})
    kwargs.pop("hashing_kv", None)
    input_prompt = ""
    try:
        input_prompt = hf_tokenizer.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )
    except Exception:
        try:
            ori_message = copy.deepcopy(messages)
            if messages[0]["role"] == "system":
                messages[1]["content"] = (
                    "<system>"
                    + messages[0]["content"]
                    + "</system>\n"
                    + messages[1]["content"]
                )
                messages = messages[1:]
                input_prompt = hf_tokenizer.apply_chat_template(
                    messages, tokenize=False, add_generation_prompt=True
                )
        except Exception:
            len_message = len(ori_message)
            for msgid in range(len_message):
                input_prompt = (
                    input_prompt
                    + "<"
                    + ori_message[msgid]["role"]
                    + ">"
                    + ori_message[msgid]["content"]
                    + "</"
                    + ori_message[msgid]["role"]
                    + ">\n"
                )

    input_ids = hf_tokenizer(
        input_prompt, return_tensors="pt", padding=True, truncation=True
    ).to("cuda")
    inputs = {k: v.to(hf_model.device) for k, v in input_ids.items()}
    output = hf_model.generate(
        **input_ids, max_new_tokens=512, num_return_sequences=1, early_stopping=True
    )
    response_text = hf_tokenizer.decode(
        output[0][len(inputs["input_ids"][0]) :], skip_special_tokens=True
    )

    return response_text


async def hf_model_complete(
    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
    keyword_extraction = kwargs.pop("keyword_extraction", None)
    model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
    result = await hf_model_if_cache(
        model_name,
        prompt,
        system_prompt=system_prompt,
        history_messages=history_messages,
        **kwargs,
    )
    if keyword_extraction:  # TODO: use JSON API
        return locate_json_string_body_from_string(result)
    return result


async def hf_embed(texts: list[str], tokenizer, embed_model) -> np.ndarray:
    device = next(embed_model.parameters()).device
    input_ids = tokenizer(
        texts, return_tensors="pt", padding=True, truncation=True
    ).input_ids.to(device)
    with torch.no_grad():
        outputs = embed_model(input_ids)
        embeddings = outputs.last_hidden_state.mean(dim=1)
    if embeddings.dtype == torch.bfloat16:
        return embeddings.detach().to(torch.float32).cpu().numpy()
    else:
        return embeddings.detach().cpu().numpy()