"""Run codes."""
# pylint: disable=line-too-long, broad-exception-caught, invalid-name, missing-function-docstring, too-many-instance-attributes, missing-class-docstring
# ruff: noqa: E501
import gc
import os
import platform
import random
import time
from dataclasses import asdict, dataclass, field
from pathlib import Path

# from types import SimpleNamespace
import gradio as gr
import psutil
from about_time import about_time
from ctransformers import AutoModelForCausalLM
from dl_hf_model import dl_hf_model
from loguru import logger

from examples_list import examples_list

url = "https://huggingface.co./TheBloke/WizardLM-1.0-Uncensored-Llama2-13B-GGML/blob/main/wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q4_K_S.bin"  # 7.37G, Max RAM required 9.87 GB

LLM = None
gc.collect()
try:
    model_loc, file_size = dl_hf_model(url)
except Exception as exc_:
    logger.error(exc_)
    raise SystemExit(1) from exc_

# raise SystemExit(0)

# Prompt template: Guanaco
# {past_history}
prompt_template = """You are a helpful assistant. Let's think step by step.
### Human:
{question}
### Assistant:"""
human_prefix = "### Human"
ai_prefix = "### Assistant"
stop_list = [f"{human_prefix}:"]

# Prompt template: WizardLM-Vicuna
prompt_template = """You are a helpful AI assistant.

USER: {question}
ASSISTANT: """
human_prefix = "USER"
ai_prefix = "ASSISTANT"
stop_list = [f"{human_prefix}:"]

_ = psutil.cpu_count(logical=False) - 1
cpu_count: int = int(_) if _ else 1
logger.debug(f"{cpu_count=}")


logger.debug(f"{model_loc=}")
LLM = AutoModelForCausalLM.from_pretrained(
    model_loc,
    model_type="llama",
    threads=cpu_count,
)

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


@dataclass
class GenerationConfig:
    temperature: float = 0.7
    top_k: int = 50
    top_p: float = 0.9
    repetition_penalty: float = 1.0
    max_new_tokens: int = 512
    seed: int = 42
    reset: bool = False
    stream: bool = True
    threads: int = cpu_count
    stop: list[str] = field(default_factory=lambda: stop_list)


def generate(
    question: str,
    llm=LLM,
    config: GenerationConfig = GenerationConfig(),
):
    """Run model inference, will return a Generator if streaming is true."""
    # _ = prompt_template.format(question=question)
    # print(_)

    prompt = prompt_template.format(question=question)

    return llm(
        prompt,
        **asdict(config),
    )


logger.debug(f"{asdict(GenerationConfig())=}")


def user(user_message, history):
    # return user_message, history + [[user_message, None]]
    if history is None:
        history = []
    history.append([user_message, None])
    return user_message, history  # keep user_message


def user1(user_message, history):
    # return user_message, history + [[user_message, None]]
    if history is None:
        history = []
    history.append([user_message, None])
    return "", history  # clear user_message


def bot_(history):
    user_message = history[-1][0]
    resp = random.choice(["How are you?", "I love you", "I'm very hungry"])
    bot_message = user_message + ": " + resp
    history[-1][1] = ""
    for character in bot_message:
        history[-1][1] += character
        time.sleep(0.02)
        yield history

    history[-1][1] = resp
    yield history


def bot(history):
    user_message = ""
    try:
        user_message = history[-1][0]
    except Exception as exc:
        logger.error(exc)
    response = []

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

    with about_time() as atime:  # type: ignore
        flag = 1
        prefix = ""
        then = time.time()

        logger.debug("about to generate")

        config = GenerationConfig(reset=True)
        for elm in generate(user_message, config=config):
            if flag == 1:
                logger.debug("in the loop")
                prefix = f"({time.time() - then:.2f}s) "
                flag = 0
                print(prefix, end="", flush=True)
                logger.debug(f"{prefix=}")
            print(elm, end="", flush=True)
            # logger.debug(f"{elm}")

            response.append(elm)
            history[-1][1] = prefix + "".join(response)
            yield history
    if response:
        _ = (
            f"(time elapsed: {atime.duration_human}, "  # type: ignore
            f"{atime.duration/len(''.join(response)):.2f}s/char)"  # type: ignore
        )
    else:
        _ = (
            "bummer...nothing generated "
            f"(time elapsed: {atime.duration_human})"  # type: ignore
        )

    history[-1][1] = "".join(response) + f"\n{_}"
    yield history


def predict_api(prompt):
    logger.debug(f"{prompt=}")
    try:
        # user_prompt = prompt
        config = GenerationConfig(
            temperature=0.2,
            top_k=10,
            top_p=0.9,
            repetition_penalty=1.0,
            max_new_tokens=512,  # adjust as needed
            seed=42,
            reset=True,  # reset history (cache)
            stream=False,
            # threads=cpu_count,
            # stop=prompt_prefix[1:2],
        )

        response = generate(
            prompt,
            config=config,
        )

        logger.debug(f"api: {response=}")
    except Exception as exc:
        logger.error(exc)
        response = f"{exc=}"
    # bot = {"inputs": [response]}
    # bot = [(prompt, response)]

    return response


css = """
    .importantButton {
        background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
        border: none !important;
    }
    .importantButton:hover {
        background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
        border: none !important;
    }
    .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;}
    .xsmall {font-size: x-small;}
"""

logger.info("start block")

with gr.Blocks(
    title=f"{Path(model_loc).name}",
    # theme=gr.themes.Soft(text_size="sm", spacing_size="sm"),
    theme=gr.themes.Glass(text_size="sm", spacing_size="sm"),
    css=css,
) as block:
    # buff_var = gr.State("")
    with gr.Accordion("🎈 Info", open=False):
        gr.Markdown(
            f"""<h5><center>{Path(model_loc).name}</center></h4>
            Most examples are meant for another model.
            You probably should try to test
            some related prompts.""",
            elem_classes="xsmall",
        )

    # chatbot = gr.Chatbot().style(height=700)  # 500
    chatbot = gr.Chatbot(height=500)

    # buff = gr.Textbox(show_label=False, visible=True)

    with gr.Row():
        with gr.Column(scale=5):
            msg = gr.Textbox(
                label="Chat Message Box",
                placeholder="Ask me anything (press Shift+Enter or click Submit to send)",
                show_label=False,
                # container=False,
                lines=6,
                max_lines=30,
                show_copy_button=True,
                # ).style(container=False)
            )
        with gr.Column(scale=1, min_width=50):
            with gr.Row():
                submit = gr.Button("Submit", elem_classes="xsmall")
                stop = gr.Button("Stop", visible=True)
                clear = gr.Button("Clear History", visible=True)
    with gr.Row(visible=False):
        with gr.Accordion("Advanced Options:", open=False):
            with gr.Row():
                with gr.Column(scale=2):
                    system = gr.Textbox(
                        label="System Prompt",
                        value=prompt_template,
                        show_label=False,
                        container=False,
                        # ).style(container=False)
                    )
                with gr.Column():
                    with gr.Row():
                        change = gr.Button("Change System Prompt")
                        reset = gr.Button("Reset System Prompt")

    with gr.Accordion("Example Inputs", open=True):
        examples = gr.Examples(
            examples=examples_list,
            inputs=[msg],
            examples_per_page=40,
        )

    # with gr.Row():
    with gr.Accordion("Disclaimer", open=False):
        _ = Path(model_loc).name
        gr.Markdown(
            f"Disclaimer: {_} can produce factually incorrect output, and should not be relied on to produce "
            "factually accurate information. {_} was trained on various public datasets; while great efforts "
            "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
            "biased, or otherwise offensive outputs.",
            elem_classes=["disclaimer"],
        )

    msg_submit_event = msg.submit(
        # fn=conversation.user_turn,
        fn=user,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=True,
        show_progress="full",
        # api_name=None,
    ).then(bot, chatbot, chatbot, queue=True)
    submit_click_event = submit.click(
        # fn=lambda x, y: ("",) + user(x, y)[1:],  # clear msg
        fn=user1,  # clear msg
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=True,
        # queue=False,
        show_progress="full",
        # api_name=None,
    ).then(bot, chatbot, chatbot, queue=True)
    stop.click(
        fn=None,
        inputs=None,
        outputs=None,
        cancels=[msg_submit_event, submit_click_event],
        queue=False,
    )
    clear.click(lambda: None, None, chatbot, queue=False)

    with gr.Accordion("For Chat/Translation API", open=False, visible=False):
        input_text = gr.Text()
        api_btn = gr.Button("Go", variant="primary")
        out_text = gr.Text()

    api_btn.click(
        predict_api,
        input_text,
        out_text,
        api_name="api",
    )

    # block.load(update_buff, [], buff, every=1)
    # block.load(update_buff, [buff_var], [buff_var, buff], every=1)

# concurrency_count=5, max_size=20
# max_size=36, concurrency_count=14
# CPU cpu_count=2 16G, model 7G
# CPU UPGRADE cpu_count=8 32G, model 7G

# does not work
_ = """
# _ = int(psutil.virtual_memory().total / 10**9 // file_size - 1)
# concurrency_count = max(_, 1)
if psutil.cpu_count(logical=False) >= 8:
    # concurrency_count = max(int(32 / file_size) - 1, 1)
else:
    # concurrency_count = max(int(16 / file_size) - 1, 1)
# """

# default concurrency_count = 1
# block.queue(concurrency_count=concurrency_count, max_size=5).launch(debug=True)

server_port = 7860
if "forindo" in platform.node():
    server_port = 7861
block.queue(max_size=5).launch(
    debug=True, server_name="0.0.0.0", server_port=server_port
)

# block.queue(max_size=5).launch(debug=True, server_name="0.0.0.0")