import os
from pathlib import Path
from dataclasses import dataclass, asdict
from ctransformers import AutoModelForCausalLM, AutoConfig


@dataclass
class GenerationConfig:
    temperature: float
    top_k: int
    top_p: float
    repetition_penalty: float
    max_new_tokens: int
    reset: bool
    stream: bool
    threads: int
    stop: list[str]


def format_prompt(user_prompt: str):
    return f"""### Instruction:
{user_prompt}

### Response:"""


def generate(llm: AutoModelForCausalLM,
             generation_config: GenerationConfig,
             prompt: str):
    return llm(format_prompt(prompt), **asdict(generation_config))


def generate_code(prompt, model_name, max_tokens, temperature):
    from_local = False

    model_path = model_name
    config_path = model_name
    if from_local:
        config_folder = model_name.split("/")[0]
        config_path = os.path.abspath(f"models/{config_folder}")
        model_path = os.path.abspath(f"models/{model_name}.bin")

    config = AutoConfig.from_pretrained(
        config_path,
    )
    llm = AutoModelForCausalLM.from_pretrained(
        model_path,
        model_type="replit",
        config=config,
    )

    generation_config = GenerationConfig(
        temperature=temperature,
        top_k=50,
        top_p=0.9,
        repetition_penalty=1.0,
        max_new_tokens=max_tokens,  # adjust as needed
        reset=True,  # reset history (cache)
        stream=True,  # streaming per word/token
        threads=os.cpu_count(),  # adjust for your CPU
        stop=["<|endoftext|>"],
    )

    generator = generate(llm, generation_config, prompt)
    output = ""
    for word in generator:
        print(word)
        output += word
    return output