"""Test various models."""
# pylint: disable=invalid-name, line-too-long,broad-exception-caught, protected-access
import os
import time
from pathlib import Path

import gradio as gr
import torch
from loguru import logger
from transformers import AutoModel, AutoTokenizer

# ruff: noqa: E402
# os.system("pip install --upgrade torch transformers sentencepiece scipy cpm_kernels accelerate bitsandbytes loguru")

# os.system("pip install torch transformers sentencepiece loguru")



# fix timezone in Linux
os.environ["TZ"] = "Asia/Shanghai"
try:
    time.tzset()  # type: ignore # pylint: disable=no-member
except Exception:
    # Windows
    logger.warning("Windows, cant run time.tzset()")

model_name = "THUDM/chatglm2-6b-int4"  # 3.9G

tokenizer = AutoTokenizer.from_pretrained(
    "THUDM/chatglm2-6b-int4", trust_remote_code=True
)

has_cuda = torch.cuda.is_available()
# has_cuda = False  # force cpu

logger.debug("load")
if has_cuda:
    if model_name.endswith("int4"):
        model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
    else:
        model = (
            AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half()
        )
else:
    model = AutoModel.from_pretrained(
        model_name, trust_remote_code=True
    ).half()  # .float() .half().float()

model = model.eval()
logger.debug("done load")

# tokenizer = AutoTokenizer.from_pretrained("openchat/openchat_v2_w")
# model = AutoModelForCausalLM.from_pretrained("openchat/openchat_v2_w", load_in_8bit_fp32_cpu_offload=True, load_in_8bit=True)

# locate model file cache
cache_loc = Path("~/.cache/huggingface/hub").expanduser()
model_cache_path = [elm for elm in Path(cache_loc).rglob("*") if Path(model_name).name in elm.as_posix() and "pytorch_model.bin" in elm.as_posix()]

logger.debug(f"{model_cache_path=}")

if model_cache_path:
    model_size_gb = Path(model_cache_path[0]).stat().st_size / 2**30
    logger.info(f"{model_name=} {model_size_gb=:.2f} GB")


def respond(message, chat_history):
    response, chat_history = model.chat(tokenizer, message, history=chat_history, temperature=0.7, repetition_penalty=1.2, max_length=128)
    chat_history.append((message, response))
    return "", chat_history


with gr.Blocks() as block:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()

clear = gr.ClearButton([msg, chatbot])
msg.submit(respond, [msg, chatbot], [msg, chatbot])

block.queue().launch()