"""Create and return a model."""
# ruff: noqa: F841

import os
import re
from platform import node

from loguru import logger
from smolagents import InferenceClientModel as HfApiModel
from smolagents import LiteLLMRouterModel, OpenAIServerModel

# FutureWarning: HfApiModel was renamed to InferenceClientModel in version 1.14.0 and will be removed in 1.17.0.
from get_gemini_keys import get_gemini_keys


def get_model(cat: str = "hf", provider=None, model_id=None):
    """
    Create and return a model.

    Args:
        cat: category, hf, gemin, llama (default and fallback: hf)
        provider: for HfApiModel (cat='hf')
        model_id: model name

    if no gemini_api_keys, return HfApiModel()

    """
    if cat.lower() in ["hf"]:
        logger.info(" usiing HfApiModel, make sure you set HF_TOKEN")
        return HfApiModel(provider=provider, model_id=model_id)

    # setup proxy for gemini and for golay (local tetsin)
    if "golay" in node() and cat.lower() in ["gemini", "llama"]:
        os.environ.update(
            HTTPS_PROXY="http://localhost:8081",
            HTTP_PROXY="http://localhost:8081",
            ALL_PROXY="http://localhost:8081",
            NO_PROXY="localhost,127.0.0.1",
        )

    if cat.lower() in ["gemini"]:
        # get gemini_api_keys
        # dedup
        _ = re.findall(r"AIzaSy[A-Z][\w-]{32}", os.getenv("GEMINI_API_KEYS", ""))
        gemini_api_keys = [*dict.fromkeys(get_gemini_keys() + _)]

        # assert gemini_api_keys, "No GEMINI_API_KEYS, set env var GEMINI_API_KEYS or put them in .env-gemini and try again."
        if not gemini_api_keys:
            logger.warning("cat='gemini' but no GEMINI_API_KEYS found,  returning HfApiModel()... Set env var GEMINI_API_KEYS and/or .env-gemini  with free space gemini-api-keys if you want to try 'gemini' ")
            logger.info(" set gemini but return HfApiModel()")
            return HfApiModel()

        if model_id is None:
            model_id = "gemini-2.5-flash-preview-04-17"

        # model_id = "gemini-2.5-flash-preview-04-17"
        llm_loadbalancer_model_list_gemini = []
        for api_key in gemini_api_keys:
            llm_loadbalancer_model_list_gemini.append(
                {
                    "model_name": "model-group-1",
                    "litellm_params": {
                        "model": f"gemini/{model_id}",
                        "api_key": api_key,
                    },
                },
            )

        model_id = "deepseek-ai/DeepSeek-V3"
        llm_loadbalancer_model_list_siliconflow = [
            {
                "model_name": "model-group-2",
                "litellm_params": {
                    "model": f"openai/{model_id}",
                    "api_key": os.getenv("SILICONFLOW_API_KEY"),
                    "api_base": "https://api.siliconflow.cn/v1",
                },
            },
        ]

        # gemma-3-27b-it
        llm_loadbalancer_model_list_gemma = [
            {
                "model_name": "model-group-3",
                "litellm_params": {
                    "model": "gemini/gemma-3-27b-it",
                    "api_key": os.getenv("GEMINI_API_KEY")                },
            },
        ]

        fallbacks = []
        model_list = llm_loadbalancer_model_list_gemini
        if os.getenv("SILICONFLOW_API_KEY"):
            fallbacks = [{"model-group-1": "model-group-2"}]
            model_list += llm_loadbalancer_model_list_siliconflow

        model_list += llm_loadbalancer_model_list_gemma
        fallbacks13 = [{"model-group-1": "model-group-3"}]
        fallbacks31 = [{"model-group-3": "model-group-1"}]

        model = LiteLLMRouterModel(
            model_id="model-group-1",
            model_list=model_list,
            client_kwargs={
                "routing_strategy": "simple-shuffle",
                "num_retries": 3,
                "retry_after": 180,   # waits min  s before retrying request
                "fallbacks": fallbacks13,  # falllacks dont seem to work
            },
        )

        if os.getenv("SILICONFLOW_API_KEY"):
            logger.info(" set gemini, return LiteLLMRouterModel + fallbacks")
        else:
            logger.info(" set gemini, return LiteLLMRouterModel")

        return model

    if cat.lower() in ["llama"]:
        api_key = os.getenv("LLAMA_API_KEY")
        if api_key is None:
            logger.warning(" LLAMA_API_EY not set, using HfApiModel(), make sure you set HF_TOKEN")
            return HfApiModel()

        # default model_id
        if model_id is None:
            model_id = "Llama-4-Maverick-17B-128E-Instruct-FP8"
            model_id = "Llama-4-Scout-17B-16E-Instruct-FP8"
        model_llama = OpenAIServerModel(
            model_id,
            api_base="https://api.llama.com/compat/v1",
            api_key=api_key,
            # temperature=0.,
        )
        return model_llama

    logger.info(" default return default HfApiModel(provider=None, model_id=None)")
    # if cat.lower() in ["hf"]:  default
    return HfApiModel(provider=provider, model_id=model_id)