import os
import re
import time
import torch
import msgpack
import numpy as np
import streamlit as st
from numpy import ndarray
from transformers import AutoModelForMaskedLM, AutoTokenizer
from scipy.sparse import csr_matrix, save_npz, load_npz, vstack
from qdrant_client import QdrantClient, models
from langchain_community.llms.llamacpp import LlamaCpp
from langchain_community.document_loaders.wikipedia import WikipediaLoader
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
from langchain_core.prompts import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain_experimental.text_splitter import SemanticChunker
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.documents import Document
from huggingface_hub import hf_hub_download
from qdrant_client.models import (
    NamedSparseVector,
    NamedVector,
    SparseVector,
    PointStruct,
    SearchRequest,
    ScoredPoint,
)

MAP_PROMPT = """
You will be given a single passage of a book. This section will be enclosed in triple backticks (```)
Your goal is to give a summary of this section so that a reader will have a full understanding of what happened.
Your response should be at least three paragraphs and fully encompass what said in the passage.

```{text}```
FULL SUMMARY:
"""

COMBINE_PROMPT = """
You will be given a series of summaries from a book. The summaries will be enclosed in triple backticks (```)
Your goal is to give a verbose summary of what happened in the story.
The reader should be able to grasp what happened in the book.

```{text}```
VERBOSE SUMMARY:
"""


def make_points(chunks: list[str], dense: list[ndarray], indices, values)-> list[PointStruct]:
    points = []
    for idx, (indice, value, chunk, dense_vector) in enumerate(zip(indices, values, chunks, dense)):
        sparse_vector = SparseVector(indices=indice.tolist(), values=value.tolist())
        point = PointStruct(
            id=idx,
            vector={
                "text-sparse": sparse_vector,
                "text-dense": dense_vector,
            },
            payload={
                "text": chunk
            }
        )
        points.append(point)
    return points

def search(client: QdrantClient, collection_name: str, dense, indices, values):
    search_results = client.search_batch(
        collection_name,
        [
            SearchRequest(
                vector=NamedVector(
                    name="text-dense",
                    vector=dense,
                ),
                limit=10
            ),
            SearchRequest(
                vector=NamedSparseVector(
                    name="text-sparse",
                    vector=SparseVector(
                        indices=indices.tolist(),
                        values=values.tolist(),
                    ),
                ),
                limit=10
            ),
        ],
    )

    return search_results

def rank_list(search_result: list[ScoredPoint]):
    return [(point.id, rank + 1) for rank, point in enumerate(search_result)]

def rrf(rank_lists, alpha=60, default_rank=1000):
    """
    Optimized Reciprocal Rank Fusion (RRF) using NumPy for large rank lists.

    :param rank_lists: A list of rank lists. Each rank list should be a list of (item, rank) tuples.
    :param alpha: The parameter alpha used in the RRF formula. Default is 60.
    :param default_rank: The default rank assigned to items not present in a rank list. Default is 1000.
    :return: Sorted list of items based on their RRF scores.
    """
    all_items = set(item for rank_list in rank_lists for item, _ in rank_list)
    item_to_index = {item: idx for idx, item in enumerate(all_items)}
    rank_matrix = np.full((len(all_items), len(rank_lists)), default_rank)

    for list_idx, rank_list in enumerate(rank_lists):
        for item, rank in rank_list:
            rank_matrix[item_to_index[item], list_idx] = rank

    rrf_scores = np.sum(1.0 / (alpha + rank_matrix), axis=1)
    sorted_indices = np.argsort(-rrf_scores)
    sorted_items = [(list(item_to_index.keys())[idx], rrf_scores[idx]) for idx in sorted_indices]

    return sorted_items


def main(query: str, client: QdrantClient, collection_name: str, llm, dense_model, sparse_model, sparse_tokenizer):
     # name = 'Kia_EV6'
    # filepath = os.path.join(os.getcwd(), name + '.pdf')

    # docs = UnstructuredFileLoader(
    #     file_path=filepath,
    #     mode='elements',
    #     strategy='hi_res',
    #     skip_infer_table_types=['png', 'pdf', 'jpg', 'xls', 'xlsx', 'heic'],
    #     hi_res_model_name='yolox',
    #     include_page_breaks=True
    # )

    # docs = docs.load()

    
    dense_query = compute_dense_query(query, dense_model)
    sparse_query = compute_sparse(query, sparse_model, sparse_tokenizer)

    search_results = search(
        client,
        collection_name,
        dense_query,
        sparse_query
    )

    dense_rank_list, sparse_rank_list = rank_list(search_results[0]), rank_list(search_results[1])
    rrf_rank_list = rrf([dense_rank_list, sparse_rank_list])

    records_list = client.retrieve(
        collection_name,
        [item[0] for item in rrf_rank_list]
    )
    
    docs = [Document(record.payload['text']) for record in records_list[:3]]

    map_prompt = PromptTemplate(
        template=MAP_PROMPT,
        input_variables=['text']
    )
    combine_prompt = PromptTemplate(
        template=COMBINE_PROMPT,
        input_variables=['text']
    )

    map_chain = load_summarize_chain(llm,
                                     "stuff",
                                     prompt=map_prompt
                                     )
    
    summary_list = []

    for doc in docs:
        chunk_summary = map_chain.invoke([doc])
        summary_list.append(chunk_summary['output_text'])

    summaries = Document(page_content="\n".join(summary_list))
    
    reduce_chain = load_summarize_chain(llm,
                                        "stuff",
                                        prompt=combine_prompt
                                        )
    
    output = reduce_chain.invoke([summaries])
    return output['output_text']

def compute_sparse(sentence, model, tokenizer):
    inputs = tokenizer(sentence, return_tensors='pt')
    inputs = {key: val.to(device) for key, val in inputs.items()}
    input_ids = inputs['input_ids']
    
    attention_mask = inputs['attention_mask']
    
    outputs = model(**inputs)
    
    logits, attention_mask = outputs.logits, attention_mask
    relu_log = torch.log(1 + torch.relu(logits))
    weighted_log = relu_log * attention_mask.unsqueeze(-1)
    max_val, _ = torch.max(weighted_log, dim=1)
    vector = max_val.squeeze()
    
    cols = vector.nonzero().squeeze().tolist()
    weights = vector[cols].tolist()

    return cols, weights

def compute_dense_query(sentence, model):
    return model.embed_query(f'Represent this sentence for searching relevant passages: {sentence}')

def compute_dense_docs(docs, model):
    return model.embed_documents(docs)

def load_models_and_documents():
    with st.spinner('Load models...'):
        model_path = hf_hub_download(repo_id='NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF',
                                     filename='Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q8_0.gguf'
                                     )
        
        llm = LlamaCpp(
            model_path=model_path,
            n_ctx=8192,
            max_tokens=3000,
            n_gpu_layers=-1,
            n_batch=512,
            f16_kv=True
        )
        
        sparse_tokenizer = AutoTokenizer.from_pretrained('prithivida/Splade_PP_en_v2')
        reverse_voc = {v: k for k, v in tokenizer.vocab.items()}
        sparse_model = AutoModelForMaskedLM.from_pretrained('prithivida/Splade_PP_en_v2')

        dense_model = HuggingFaceEmbeddings(model_name='mixedbread-ai/mxbai-embed-large-v1', 
                                            cache_folder=os.getenv('HF_HOME'), 
                                            model_kwargs={'truncate_dim':512}
                                           )

    client = QdrantClient(path=os.getenv('HF_HOME'))
    collection_name = 'collection_demo'
    
    if not client.collection_exists(collection_name):
        client.create_collection(
            collection_name,
            {
                "text-dense": models.VectorParams(
                    size=1024,
                    distance=models.Distance.COSINE,
                    on_disk=True,
                    quantization_config=models.BinaryQuantization(
                        binary=models.BinaryQuantizationConfig(
                            always_ram=False
                        )
                    )
                )
            },
            {
                "text-sparse": models.SparseVectorParams(
                    index=models.SparseIndexParams(
                        on_disk=True
                    )
                )
            },
            2,
            on_disk_payload=True,
            optimizers_config=models.OptimizersConfigDiff(
                memmap_threshold=10000,
                indexing_threshold=0
            ),
            hnsw_config=models.HnswConfigDiff(
                on_disk=True, 
                m=16, 
                ef_construct=100
            )
        )
        
    with st.spinner('Parse and chunk documents...'):
        name = 'action_rpg'
        embeddings_path = os.path.join(os.getenv('HF_HOME'), 'collection', 'embeddings')

        chunks_path = os.path.join(embeddings_path, name + '_chunks.msgpack')
        dense_path = os.path.join(embeddings_path, name + '_dense.npz')
        sparse_path = os.path.join(embeddings_path, name + '_sparse.npz')

        if not os.path.exists(embeddings_path):
            os.mkdir(embeddings_path)

            docs = WikipediaLoader(query='Action-RPG').load()
            chunks, dense_embeddings, indices, values = chunk_documents(docs, dense_model, sparse_model, sparse_tokenizer)

            with open(chunks_path, "wb") as outfile:
                packed = msgpack.packb(chunks, use_bin_type=True)
                outfile.write(packed)
            
            np.savez_compressed(dense_path, *dense_embeddings)
            max_index = max(np.max(indice) for indice in indices)
        
            sparse_matrices = []
            for indice, value in zip(indices, values):
                data = value
                indices = indice
                indptr = np.array([0, len(data)])
                matrix = csr_matrix((data, indice, indptr), shape=(1, max_index + 1))
                sparse_matrices.append(matrix)
            
            combined_sparse_matrix = vstack(sparse_matrices)
            save_npz(sparse_path, combined_sparse_matrix)
        else:
            with open(chunks_path, "rb") as data_file:
                byte_data = data_file.read()
                
            chunks = msgpack.unpackb(byte_data, raw=False)

            dense_embeddings = list(np.load(dense_path).values())

            indices = []
            values = []
            loaded_sparse_matrix = load_npz(sparse_path)
        
            for i in range(loaded_sparse_matrix.shape[0]):
                row = loaded_sparse_matrix.getrow(i)
                values.append(row.data)
                indices.append(row.indices)

    with st.spinner('Save documents...'):
        client.upsert(
            collection_name,
            make_points(
                chunks, 
                dense_embeddings, 
                indices,
                values,
            )
        )
    client.update_collection(
        collection_name=collection_name,
        optimizer_config=models.OptimizersConfigDiff(indexing_threshold=20000)
    )

    return client, collection_name, llm, dense_model, sparse_model, sparse_tokenizer

def chunk_documents(docs, dense_model, sparse_model, sparse_tokenizer):
    text_splitter = SemanticChunker(
        dense_model,
        breakpoint_threshold_type='standard_deviation'
    )

    documents = [doc.page_content for doc in text_splitter.transform_documents(list(docs))]

    dense_embeddings = compute_dense_docs(documents, dense_model)
    indices, values = compute_sparse(documents, sparse_model, sparse_tokenizer)
    
    return documents, dense_embeddings, indices, values
    
if __name__ == '__main__':
    st.set_page_config(page_title="Video Game Assistant",
                       layout="wide"
                       )
    st.title("Video Game Assistant :sunglasses:")
    
    if 'models_loaded' not in st.session_state:
        st.session_state.client, st.session_state.collection_name, st.session_state.llm, st.session_state.dense_model, st.session_state.sparse_model, st.session_state.sparse_tokenizer = load_models_and_documents()
        st.session_state.models_loaded = True
    
    if "messages" not in st.session_state:
        st.session_state.messages = []

    for message in st.session_state.messages:
         with st.chat_message(message["role"]):
            st.markdown(message["content"])

    if prompt := st.chat_input("Message Video Game Assistant"):
        st.chat_message("user").markdown(prompt)
        st.session_state.messages.append({"role": "user", "content": prompt})

        ai_response = main(prompt, st.session_state.client, st.session_state.collection_name, st.session_state.llm, st.session_state.dense_model, st.session_state.sparse_model, st.session_state.sparse_tokenizer)
        response = f"Echo: {ai_response}"
        with st.chat_message("assistant"):
            message_placeholder = st.empty()
            full_response = ""
            for chunk in re.split(r'(\s+)', response):
                full_response += chunk + " "
                time.sleep(0.01)
                message_placeholder.markdown(full_response + "▌")
        st.session_state.messages.append({"role": "assistant", "content": full_response})