import os
str_cmd1 = 'pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"'
str_cmd2 = 'pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes'
os.system(str_cmd1)
os.system(str_cmd2)


from unsloth import FastLanguageModel
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

from langchain_community.llms import CTransformers
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_community.embeddings import GPT4AllEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.llms import HuggingFacePipeline
from langchain.callbacks.base import BaseCallbackHandler
from transformers import pipeline

# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
    "unsloth/mistral-7b-bnb-4bit",
    "unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
    "unsloth/llama-2-7b-bnb-4bit",
    "unsloth/llama-2-13b-bnb-4bit",
    "unsloth/codellama-34b-bnb-4bit",
    "unsloth/tinyllama-bnb-4bit",
    "unsloth/gemma-7b-bnb-4bit", # New Google 6 trillion tokens model 2.5x faster!
    "unsloth/gemma-2b-bnb-4bit",
] # More models at https://huggingface.co/unsloth

template = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
You are ResVuAssist and You are a helpful bot who reads texts and answers questions about them.

### Input:
{context}
QUESTION: {question}

### Response:
"""

# Cau hinh
vector_db_path = "vectorstores/db_faiss"


def initialModelAndTokenizer():
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name = "unsloth/mistral-7b-instruct-v0.3-bnb-4bit", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B
        max_seq_length = max_seq_length,
        dtype = dtype,
        load_in_4bit = load_in_4bit,
        # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
    )
    model = FastLanguageModel.get_peft_model(
        model,
        r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
        target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                        "gate_proj", "up_proj", "down_proj",],
        lora_alpha = 16,
        lora_dropout = 0, # Supports any, but = 0 is optimized
        bias = "none",    # Supports any, but = "none" is optimized
        # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
        use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
        random_state = 3407,
        use_rslora = False,  # We support rank stabilized LoRA
        loftq_config = None, # And LoftQ
    )
    return model, tokenizer

def create_pipeline():
    model, tokenizer = initialModelAndTokenizer()
    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        max_new_tokens=512,
        temperature=0.1,
        top_p=0.95,
        repetition_penalty=1.15
    )
    return pipe

# Tao prompt template
def creat_prompt(template):
    prompt = PromptTemplate(template = template, input_variables=["context", "question"])
    return prompt

# Tao simple chain
def create_qa_chain(prompt, llm, db):
    llm_chain = RetrievalQA.from_chain_type(
        llm = llm,
        chain_type= "stuff",
        # retriever = db.as_retriever(search_kwargs = {"k":8}, max_tokens_limit=1024),
        retriever = db.as_retriever(search_kwargs = {"k": 15}, max_tokens_limit=4096),
        return_source_documents = False,
        chain_type_kwargs= {'prompt': prompt},
    )
    return llm_chain

# Read tu VectorDB
def read_vectors_db():
    # Embeding
    embedding_model = GPT4AllEmbeddings(model_name="all-MiniLM-L6-v2.gguf2.f16.gguf")
    db = FAISS.load_local(vector_db_path, embedding_model,  allow_dangerous_deserialization=True)
    return db

def get_response_value(text):
    start = text.find('### Response:')
    if start != -1:
        return text[start + len('### Response:'):].strip()
    return None

def llm_chain_response():
    pipe = create_pipeline()
    db = read_vectors_db()
    prompt = creat_prompt(template)
    llm = HuggingFacePipeline(pipeline=pipe)

    llm_chain  =create_qa_chain(prompt, llm, db)
    return llm_chain