import os
import platform
import random
import time
from dataclasses import asdict, dataclass
from pathlib import Path

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


URL = "https://huggingface.co/s3nh/TinyGauss-1.1B-GGUF/resolve/main/tinygauss-1.1b.Q4_K_M.gguf"  # 4.05G

_ = (
    "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 = URL  #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]
"""

prompt_template = """### HUMAN:
{question}
### RESPONSE:"""


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




prompt_template = """
HUMAN: {question}
ASSISTANT:"""


prompt_template = """<|prompt|>:{question}</s>
<|answer|>:"""

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

logger.debug(f"{stop_string=} not used")

_ = 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",
)

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

os.environ["TZ"] = "Asia/Shanghai"
try:
    time.tzset() 

    logger.warning("Windows, cant run time.tzset()")
except Exception:
    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_string])


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


    prompt = prompt_template.format(question=question)

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


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


def user(user_message, history):
    history.append([user_message, None])
    return user_message, history 


def user1(user_message, history):
    history.append([user_message, None])
    return "", history  

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: 
        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)

            response.append(elm)
            history[-1][1] = prefix + "".join(response)
            yield history

    _ = (
        f"(time elapsed: {atime.duration_human}, "  
        f"{atime.duration/len(''.join(response)):.2f}s/char)"  
    )

    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,  
            stream=False,
        )

        response = generate(
            prompt,
            config=config,
        )

        logger.debug(f"api: {response=}")
    except Exception as exc:
        logger.error(exc)
        response = f"{exc=}"
    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;}
"""
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 = [
     ["Send an email requesting that people use language models responsibly."],
    ["Write a shouting match between Julius Caesar and Napoleon"],
    ["Write a theory to explain why cat never existed"], 
    ["write a story about a grain of sand as it watches millions of years go by"], 
    ["What are 3 popular chess openings?"], 
    ["write a conversation between the sun and pluto"],
    ["Did you know that Yann LeCun dropped a rap album last year? We listened to it andhere’s what we thought:"],
]

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=False):
        # 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>
            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=True):
        _ = Path(model_loc).name
        gr.Markdown(
            "Disclaimer: I AM NOT RESPONSIBLE FOR ANY PROMPT PROVIDED BY USER AND PROMPT RETURNED FROM THE MODEL. THIS APP SHOULD BE USED FOR EDUCATIONAL PURPOSE"
            "WITHOUT ANY OFFENSIVE, AGGRESIVE INTENTS. {_} can produce factually incorrect output, and should not be relied on to produce "
            f"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().launch(debug=True)