import sys
import os
from contextlib import contextmanager

from langchain_core.tools import tool
from langchain_core.runnables import chain
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.runnables import RunnableLambda

from ..reranker import rerank_docs
from ...knowledge.retriever import ClimateQARetriever
from ...knowledge.openalex import OpenAlexRetriever
from .keywords_extraction import make_keywords_extraction_chain
from ..utils import log_event



def divide_into_parts(target, parts):
    # Base value for each part
    base = target // parts
    # Remainder to distribute
    remainder = target % parts
    # List to hold the result
    result = []
    
    for i in range(parts):
        if i < remainder:
            # These parts get base value + 1
            result.append(base + 1)
        else:
            # The rest get the base value
            result.append(base)
    
    return result


@contextmanager
def suppress_output():
    # Open a null device
    with open(os.devnull, 'w') as devnull:
        # Store the original stdout and stderr
        old_stdout = sys.stdout
        old_stderr = sys.stderr
        # Redirect stdout and stderr to the null device
        sys.stdout = devnull
        sys.stderr = devnull
        try:
            yield
        finally:
            # Restore stdout and stderr
            sys.stdout = old_stdout
            sys.stderr = old_stderr


@tool
def query_retriever(question):
    """Just a dummy tool to simulate the retriever query"""
    return question







def make_retriever_node(vectorstore,reranker,llm,rerank_by_question=True, k_final=15, k_before_reranking=100, k_summary=5):

    # The chain callback is not necessary, but it propagates the langchain callbacks to the astream_events logger to display intermediate results
    @chain
    async def retrieve_documents(state,config):

        keywords_extraction = make_keywords_extraction_chain(llm)
        
        current_question = state["remaining_questions"][0]
        remaining_questions = state["remaining_questions"][1:]
        
        # ToolMessage(f"Retrieving documents for question: {current_question['question']}",tool_call_id = "retriever")


        # # There are several options to get the final top k
        # # Option 1 - Get 100 documents by question and rerank by question
        # # Option 2 - Get 100/n documents by question and rerank the total
        # if rerank_by_question:
        #     k_by_question = divide_into_parts(k_final,len(questions))
        
        # docs = state["documents"]
        # if docs is None: docs = []

        docs = []
        k_by_question = k_final // state["n_questions"]
        
        sources = current_question["sources"]
        question = current_question["question"]
        index = current_question["index"]
        

        await log_event({"question":question,"sources":sources,"index":index},"log_retriever",config)


        if index == "Vector":
                
            # Search the document store using the retriever
            # Configure high top k for further reranking step
            retriever = ClimateQARetriever(
                vectorstore=vectorstore,
                sources = sources,
                min_size = 200,
                k_summary = k_summary,
                k_total = k_before_reranking,
                threshold = 0.5,
            )
            docs_question = await retriever.ainvoke(question,config)

        elif index == "OpenAlex":

            keywords = keywords_extraction.invoke(question)["keywords"]
            openalex_query = " AND ".join(keywords)

            print(f"... OpenAlex query: {openalex_query}")

            retriever_openalex = OpenAlexRetriever(
                min_year = state.get("min_year",1960), 
                max_year = state.get("max_year",None), 
                k = k_before_reranking
            )
            docs_question = await retriever_openalex.ainvoke(openalex_query,config)

        else:
            raise Exception(f"Index {index} not found in the routing index")
        
        # Rerank
        if reranker is not None:
            with suppress_output():
                docs_question = rerank_docs(reranker,docs_question,question)
        else:
            # Add a default reranking score
            for doc in docs_question:
                doc.metadata["reranking_score"] = doc.metadata["similarity_score"]
            
        # If rerank by question we select the top documents for each question
        if rerank_by_question:
            docs_question = docs_question[:k_by_question]
            
        # Add sources used in the metadata
        for doc in docs_question:
            doc.metadata["sources_used"] = sources
            doc.metadata["question_used"] = question
            doc.metadata["index_used"] = index
        
        # Add to the list of docs
        docs.extend(docs_question)
            
        # Sorting the list in descending order by rerank_score
        docs = sorted(docs, key=lambda x: x.metadata["reranking_score"], reverse=True)
        new_state = {"documents":docs,"remaining_questions":remaining_questions}
        return new_state
    
    return retrieve_documents