"""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 pendulum
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).float()
    )  # .float() .half().float(): must use float for cpu

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

def get_time():
    # return datetime.now().time()
    return pendulum.now().format('HH:mm:ss zz')

def respond(message, chat_history):
    """Gen a response."""
    message = message.strip()
    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 message, chat_history


theme = gr.themes.Soft(text_size="sm")
with gr.Blocks(theme=theme) as block:
    chatbot = gr.Chatbot()

    with gr.Column():
        with gr.Column(scale=12):
            msg = gr.Textbox()
        _ = """
        with gr.Column(scale=1, min_width=16):
            btn = gr.Button("Send")
        with gr.Column(scale=1, min_width=8):
            clear = gr.ClearButton([msg, chatbot])
        with gr.Column(scale=1, min_width=25):
            dt = gr.Textbox(label="Current time")
        # """
        with gr.Column(scale=1, min_width=100):
            with gr.Column():
                with gr.Column(scale=1, min_width=50):
                    btn = gr.Button("Send")
                with gr.Column(scale=1, min_width=50):
                    clear = gr.ClearButton([msg, chatbot])
            # with gr.Row():
            dt = gr.Textbox(label="Current time")
    # do not clear prompt
    msg.submit(respond, [msg, chatbot], [msg, chatbot])

    btn.click(lambda x, y: ("",) + respond(x, y)[1:], [msg, chatbot], [msg, chatbot])

    with gr.Accordion("Example inputs", open=True):
        etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
        examples = gr.Examples(
            examples=[
                ["Explain the plot of Cinderella in a sentence."],
                [
                    "How long does it take to become proficient in French, and what are the best methods for retaining information?"
                ],
                ["What are some common mistakes to avoid when writing code?"],
                ["Build a prompt to generate a beautiful portrait of a horse"],
                ["Suggest four metaphors to describe the benefits of AI"],
                ["Write a pop song about leaving home for the sandy beaches."],
                ["Write a summary demonstrating my ability to tame lions"],
                ["鲁迅和周树人什么关系"],
                ["从前有一头牛，这头牛后面有什么？"],
                ["正无穷大加一大于正无穷大吗？"],
                ["正无穷大加正无穷大大于正无穷大吗？"],
                ["-2的平方根等于什么"],
                ["树上有5只鸟，猎人开枪打死了一只。树上还有几只鸟？"],
                ["树上有11只鸟，猎人开枪打死了一只。树上还有几只鸟？提示：需考虑鸟可能受惊吓飞走。"],
                ["鲁迅和周树人什么关系 用英文回答"],
                ["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"],
                [f"{etext} 翻成中文，列出3个版本"],
                [f"{etext} \n 翻成中文，保留原意，但使用文学性的语言。不要写解释。列出3个版本"],
                ["js 判断一个数是不是质数"],
                ["js 实现python 的 range(10)"],
                ["js 实现python 的 [*(range(10)]"],
                ["假定 1 + 2 = 4, 试求 7 + 8"],
                ["Erkläre die Handlung von Cinderella in einem Satz."],
                ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"],
            ],
            inputs=[msg],
            examples_per_page=60,
        )
    block.load(get_time, inputs=[], outputs=dt, every=1)
block.queue().launch()