import json
import os
import time

from bfcl_eval.model_handler.base_handler import BaseHandler
from bfcl_eval.constants.type_mappings import GORILLA_TO_OPENAPI
from bfcl_eval.model_handler.model_style import ModelStyle
from bfcl_eval.model_handler.utils import (
    convert_to_function_call,
    convert_to_tool,
    func_doc_language_specific_pre_processing,
)
from openai import OpenAI


class YiHandler(BaseHandler):
    def __init__(self, model_name, temperature) -> None:
        super().__init__(model_name, temperature)
        self.model_style = ModelStyle.OpenAI
        self.base_url = "https://api.01.ai/v1"
        self.client = OpenAI(base_url=self.base_url, api_key=os.getenv("YI_API_KEY"))

    def decode_ast(self, result, language="Python"):
        decoded_output = []
        for invoked_function in result:
            name = list(invoked_function.keys())[0]
            params = json.loads(invoked_function[name])
            decoded_output.append({name: params})

        return decoded_output

    def decode_execute(self, result):
        function_call = convert_to_function_call(result)
        return function_call

    #### FC methods ####

    def _query_FC(self, inference_data: dict):
        message: list[dict] = inference_data["message"]
        tools = inference_data["tools"]
        inference_data["inference_input_log"] = {"message": repr(message), "tools": tools}

        start_time = time.time()
        if len(tools) > 0:
            api_response = self.client.chat.completions.create(
                messages=message,
                model=self.model_name.replace("-FC", ""),
                temperature=self.temperature,
                tools=tools,
            )
        else:
            api_response = self.client.chat.completions.create(
                messages=message,
                model=self.model_name.replace("-FC", ""),
                temperature=self.temperature,
            )
        end_time = time.time()

        return api_response, end_time - start_time

    def _pre_query_processing_FC(self, inference_data: dict, test_entry: dict) -> dict:
        inference_data["message"] = []
        return inference_data

    def _compile_tools(self, inference_data: dict, test_entry: dict) -> dict:
        functions: list = test_entry["function"]
        test_category: str = test_entry["id"].rsplit("_", 1)[0]

        functions = func_doc_language_specific_pre_processing(functions, test_category)
        tools = convert_to_tool(functions, GORILLA_TO_OPENAPI, self.model_style)

        inference_data["tools"] = tools

        return inference_data

    def _parse_query_response_FC(self, api_response: any) -> dict:
        try:
            model_responses = [
                {func_call.function.name: func_call.function.arguments}
                for func_call in api_response.choices[0].message.tool_calls
            ]
            tool_call_ids = [
                func_call.id for func_call in api_response.choices[0].message.tool_calls
            ]
            tool_call_func_names = [
                func_call.function.name
                for func_call in api_response.choices[0].message.tool_calls
            ]
        except:
            model_responses = api_response.choices[0].message.content
            tool_call_ids = []
            tool_call_func_names = []

        model_responses_message_for_chat_history = api_response.choices[0].message

        return {
            "model_responses": model_responses,
            "model_responses_message_for_chat_history": model_responses_message_for_chat_history,
            "tool_call_ids": tool_call_ids,
            "tool_call_func_names": tool_call_func_names,
            "input_token": api_response.usage.prompt_tokens,
            "output_token": api_response.usage.completion_tokens,
        }

    def add_first_turn_message_FC(
        self, inference_data: dict, first_turn_message: list[dict]
    ) -> dict:
        inference_data["message"].extend(first_turn_message)
        return inference_data

    def _add_next_turn_user_message_FC(
        self, inference_data: dict, user_message: list[dict]
    ) -> dict:
        inference_data["message"].extend(user_message)
        return inference_data

    def _add_assistant_message_FC(
        self, inference_data: dict, model_response_data: dict
    ) -> dict:
        inference_data["message"].append(
            model_response_data["model_responses_message_for_chat_history"]
        )
        return inference_data

    def _add_execution_results_FC(
        self,
        inference_data: dict,
        execution_results: list[str],
        model_response_data: dict,
    ) -> dict:
        # Add the execution results to the current round result, one at a time
        for execution_result, tool_call_id, tool_call_func_name in zip(
            execution_results,
            model_response_data["tool_call_ids"],
            model_response_data["tool_call_func_names"],
        ):
            tool_message = {
                "tool_call_id": tool_call_id,
                "role": "tool",
                "name": tool_call_func_name,
                "content": execution_result,
            }
            inference_data["message"].append(tool_message)

        return inference_data
