"""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 os
import platform
import random
import time
from dataclasses import asdict, dataclass
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

filename_list = [
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q2_K.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_L.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_M.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_S.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_S.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_1.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_M.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_S.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q6_K.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q8_0.bin",
]

URL = "https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML/raw/main/Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin"  # 4.05G

url = "https://huggingface.co/savvamadar/ggml-gpt4all-j-v1.3-groovy/blob/main/ggml-gpt4all-j-v1.3-groovy.bin"
url = "https://huggingface.co/TheBloke/Llama-2-13B-GGML/blob/main/llama-2-13b.ggmlv3.q4_K_S.bin"  # 7.37G
# url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin"
url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin"  # 6.93G
# url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.binhttps://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q4_K_M.bin"  # 7.87G

url = "https://huggingface.co/localmodels/Llama-2-13B-Chat-ggml/blob/main/llama-2-13b-chat.ggmlv3.q4_K_S.bin"  # 7.37G

_ = (
    "golay" in platform.node()
    or "okteto" in platform.node()
    or Path("/kaggle").exists()
    # or psutil.cpu_count(logical=False) < 4
    or 1  # run 7b in hf
)

if _:
    # url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q2_K.bin"
    url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q2_K.bin"  # 2.87G
    url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin"  # 2.87G


prompt_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {user_prompt}

### Response:
"""

prompt_template = """System: You are a helpful,
respectful and honest assistant. Always answer as
helpfully as possible, while being safe.  Your answers
should not include any harmful, unethical, racist,
sexist, toxic, dangerous, or illegal content. Please
ensure that your responses are socially unbiased and
positive in nature. If a question does not make any
sense, or is not factually coherent, explain why instead
of answering something not correct. If you don't know
the answer to a question, please don't share false
information.
User: {prompt}
Assistant: """

prompt_template = """System: You are a helpful assistant.
User: {prompt}
Assistant: """

prompt_template = """Question: {question}
Answer: Let's work this out in a step by step way to be sure we have the right answer."""

prompt_template = """[INST] <>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible assistant. Think step by step.
<>

What NFL team won the Super Bowl in the year Justin Bieber was born?
[/INST]"""

prompt_template = """[INST] <<SYS>>
You are an unhelpful assistant. Always answer as helpfully as possible. Think step by step. <</SYS>>

{question} [/INST]
"""

prompt_template = """[INST] <<SYS>>
You are a helpful assistant.
<</SYS>>

{question} [/INST]
"""

_ = [elm for elm in prompt_template.splitlines() if elm.strip()]
stop_string = [elm.split(":")[0] + ":" for elm in _][-2]

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

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

LLM = None

try:
    model_loc, file_size = dl_hf_model(url)
except Exception as exc_:
    logger.error(exc_)
    raise SystemExit(1) from exc_

LLM = AutoModelForCausalLM.from_pretrained(
    model_loc,
    model_type="llama",
    # threads=cpu_count,
)

logger.info(f"done load llm {model_loc=} {file_size=}G")

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

_ = """
ns = SimpleNamespace(
    response="",
    generator=(_ for _ in []),
)
# """

@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_string])


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]]
    history.append([user_message, None])
    return user_message, history  # keep user_message


def user1(user_message, history):
    # return user_message, history + [[user_message, None]]
    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 = history[-1][0]
    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

    _ = (
        f"(time elapsed: {atime.duration_human}, "  # type: ignore
        f"{atime.duration/len(''.join(response)):.2f}s/char)"  # 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;
    }
    footer {visibility: hidden}
    .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;}
    .xsmall {font-size: x-small;}
"""
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_list = [
    ["How can I start learning JavaScript? Recommend some beginner-friendly resources."],
    [
        "Suggest some book to learn Java"
    ],
    ["Explain the concept of object-oriented programming in Python."],
    ["What are the essential programming languages for web development"],
    ["Explain the importance of cybersecurity awareness and suggest resources to learn about it."],
    ["How can I improve my problem-solving skills in programming?"],
    [
        "Explain the concept of blockchain and its applications in various industries."
    ],
    ["What are some free resources to learn data science and statistical analysis?"],
    ["Explain the basics of search engine optimization (SEO) and its importance for websites."],
    ["Explain the concept of cloud computing and its benefits for businesses."],
    ["What are the best resources to learn about machine learning algorithms and their implementations?"],
    ["Suggest practical projects to enhance my coding skills and apply theoretical knowledge."],
    ["What are the best online platforms for learning languages"],
    ["Suggest resources and platforms for learning front-end web development, including HTML, CSS, and JavaScript."],
    ["What are some effective strategies for learning to code collaboratively with others?"],
    ["What are the emerging trends in virtual reality (VR) and augmented reality (AR) technologies?"],
    ["Explain http request"],
    ["what is CNN in AI?"],
    ["what is NLP in AI?"],
    ["write hello world in cpp"],
    ["write fibonacci series in JavaScript"],
    ["Explain classes in Java in short "],
    
]

logger.info("start block")

with gr.Blocks(
    title=f"{Path(model_loc).name}",
    theme=gr.themes.Soft(text_size="sm", spacing_size="sm"),
    css=css,
) as block:
    # buff_var = gr.State("")
    with gr.Accordion("🎈 Info", open=True):
        # gr.HTML(
        #     """<center><a href="https://huggingface.co/spaces/mikeee/mpt-30b-chat?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate"></a> and spin a CPU UPGRADE to avoid the queue</center>"""
        # )
        gr.Markdown(
            f"""<h5><center>{Path(model_loc).name}</center></h4>
           VecDigiChat: Level Up Your Learning - Unleashing the Power of Llama 2 Next generation Open Source LLM by Meta ! powered by LAVAN and HuggingSpace.This is a part of VEC DigiLib Project ,the DigiChat may take some time to produce output depending upon the traffic of the users and this runs on cpu so it may take some time ,this is an experimental feature!""",
            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 any doubt related to learning (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)
# """

concurrency_count = 1
logger.info(f"{concurrency_count=}")

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